Hypothesis based solicitation of data indicating at least one objective occurrence

ABSTRACT

A computationally implemented method includes, but is not limited to: soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)). All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications isincorporated herein by reference to the extent such subject matter isnot inconsistent herewith.

RELATED APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garrns, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 21 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 26 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,134, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/378,162, entitled SOLICITING DATA INDICATING ATLEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C.Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D.Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood,Jr. as inventors, filed 9 Feb. 2009, which is currently co-pending, oris an application of which a currently co-pending application isentitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/378,288, entitled SOLICITING DATA INDICATING ATLEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C.Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D.Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood,Jr. as inventors, filed 11 Feb. 2009, which is currently co-pending, oris an application of which a currently co-pending application isentitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/380,409, entitled SOLICITING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger;Jason Garns; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 25 Feb. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/380,573, entitled SOLICITING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 26 Feb. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/383,581, entitled CORRELATING DATA INDICATINGSUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATAINDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, JasonGarms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, RoyceA. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr.,Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., asinventors, filed 24 Mar. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/383,817, entitled CORRELATING DATA INDICATINGSUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATAINDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, JasonGarms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, RoyceA. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr.,Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., asinventors, filed 25 Mar. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/384,660, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 6 Apr. 2009, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/384,779, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 7 Apr. 2009, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant Entity (hereinafter “Applicant”) has providedabove a specific reference to the application(s) from which priority isbeing claimed as recited by statute. Applicant understands that thestatute is unambiguous in its specific reference language and does notrequire either a serial number or any characterization, such as“continuation” or “continuation-in-part,” for claiming priority to U.S.patent applications. Notwithstanding the foregoing, Applicantunderstands that the USPTO's computer programs have certain data entryrequirements, and hence Applicant is designating the present applicationas a continuation-in-part of its parent applications as set forth above,but expressly points out that such designations are not to be construedin any way as any type of commentary and/or admission as to whether ornot the present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Applications and of any and allparent, grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

SUMMARY

A computationally implemented method includes, but is not limited tosoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence; and acquiring the objective occurrence dataincluding the data indicating incidence of at least one objectiveoccurrence. In addition to the foregoing, other method aspects aredescribed in the claims, drawings, and text forming a part of thepresent disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for soliciting, based at least in part on a hypothesis that linksone or more objective occurrences with one or more subjective userstates and in response at least in part to an incidence of at least onesubjective user state associated with a user, at least a portion ofobjective occurrence data including data indicating incidence of atleast one objective occurrence; and means for acquiring the objectiveoccurrence data including the data indicating incidence of at least oneobjective occurrence. In addition to the foregoing, other system aspectsare described in the claims, drawings, and text forming a part of thepresent disclosure.

A computationally implemented system includes, but is not limited to:circuitry for soliciting, based at least in part on a hypothesis thatlinks one or more objective occurrences with one or more subjective userstates and in response at least in part to an incidence of at least onesubjective user state associated with a user, at least a portion ofobjective occurrence data including data indicating incidence of atleast one objective occurrence; and circuitry for acquiring theobjective occurrence data including the data indicating incidence of atleast one objective occurrence. In addition to the foregoing, othersystem aspects are described in the claims, drawings, and text forming apart of the present disclosure.

A computer program product including a signal-bearing medium bearing oneor more instructions for soliciting, based at least in part on ahypothesis that links one or more objective occurrences with one or moresubjective user states and in response at least in part to an incidenceof at least one subjective user state associated with a user, at least aportion of objective occurrence data including data indicating incidenceof at least one objective occurrence; and one or more instructions foracquiring the objective occurrence data including the data indicatingincidence of at least one objective occurrence. In addition to theforegoing, other computer program product aspects are described in theclaims, drawings, and text forming a part of the present disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1 a and 1 b show a high-level block diagram of a mobile device 30and a computing device 10 operating in a network environment.

FIG. 2 a shows another perspective of the objective occurrence datasolicitation module 101 of the computing device 10 of FIG. 1 b.

FIG. 2 b shows another perspective of the subjective user state dataacquisition module 102 of the computing device 10 of FIG. 1 b.

FIG. 2 c shows another perspective of the objective occurrence dataacquisition module 104 of the computing device 10 of FIG. 1 b.

FIG. 2 d shows another perspective of the correlation module 106 of thecomputing device 10 of FIG. 1 b.

FIG. 2 e shows another perspective of the presentation module 108 of thecomputing device 10 of FIG. 1 b.

FIG. 2 f shows another perspective of the one or more applications 126of the computing device 10 of FIG. 1 b.

FIG. 2 g shows another perspective of the mobile device 30 of FIG. 1 a.

FIG. 2 h shows another perspective of the objective occurrence datasolicitation module 101′ of the mobile device 30 of FIG. 2 g.

FIG. 2 i shows another perspective of the subjective user state dataacquisition module 102′ of the mobile device 30 of FIG. 2 g.

FIG. 2 j shows another perspective of the objective occurrence dataacquisition module 104′ of the mobile device 30 of FIG. 2 g.

FIG. 2 k shows another perspective of the presentation module 108′ ofthe mobile device 30 of FIG. 2 g.

FIG. 2 l shows another perspective of the one or more applications 126′of the mobile device 30 of FIG. 2 g.

FIG. 3 is a high-level logic flowchart of a process.

FIG. 4 a is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 b is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 c is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 d is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 e is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 f is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 g is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 h is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 i is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 4 j is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 302 of FIG. 3.

FIG. 5 a is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 b is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 c is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 5 d is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 304 of FIG. 3.

FIG. 6 is a high-level logic flowchart of another process.

FIG. 7 a is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 606 of FIG. 6.

FIG. 7 b is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 606 of FIG. 6.

FIG. 8 is a high-level logic flowchart of still another process.

FIG. 9 is a high-level logic flowchart of a process depicting alternateimplementations of the correlation operation 808 of FIG. 8.

FIG. 10 is a high-level logic flowchart of a process depicting alternateimplementations of the presentation operation 810 of FIG. 8.

FIG. 11 is a high-level logic flowchart of still another process.

FIG. 12 is a high-level logic flowchart of a process depicting alternateimplementations of the objective occurrence data transmission operation1106 of FIG. 11.

FIG. 13 is a high-level logic flowchart of a process depicting alternateimplementations of the reception operation 1108 of FIG. 11.

FIG. 14 is a high-level logic flowchart of a process depicting alternateimplementations of the presentation operation 1110 of FIG. 11.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

A recent trend that is becoming increasingly popular in thecomputing/communication field is to electronically record one'sfeelings, thoughts, and other aspects of the person's everyday life ontoan open diary. One place where such open diaries are maintained are atsocial networking sites commonly known as “blogs” where one or moreusers may report or post their thoughts and opinions on various topics,latest news, current events, and various other aspects of the users'everyday life. The process of reporting or posting blog entries iscommonly referred to as blogging. Other social networking sites mayallow users to update their personal information via, for example,social network status reports in which a user may report or post forothers to view the latest status or other aspects of the user.

A more recent development in social networking is the introduction andexplosive growth of microblogs in which individuals or users (referredto as “microbloggers”) maintain open diaries at microblog websites(e.g., otherwise known as “twitters”) by continuously orsemi-continuously posting microblog entries. A microblog entry (e.g.,“tweet”) is typically a short text message that is usually not more than140 characters long. The microblog entries posted by a microblogger mayreport on any aspect of the microblogger's daily life.

The various things that are typically posted through microblog entriesmay be categorized into one of at least two possible categories. Thefirst category of things that may be reported through microblog entriesare “objective occurrences” that may or may not be associated with themicroblogger. Objective occurrences that are associated with amicroblogger may be any characteristic, event, happening, or any otheraspects associated with or are of interest to the microblogger that canbe objectively reported by the microblogger, a third party, or by adevice. These things would include, for example, food, medicine, ornutraceutical intake of the microblogger, certain physicalcharacteristics of the microblogger such as blood sugar level or bloodpressure that can be objectively measured, daily activities of themicroblogger observable by others or by a device, performance of thestock market (which the microblogger may have an interest in), and soforth. In some cases, objective occurrences may not be at least directlyassociated with a microblogger. Examples of such objective occurrencesinclude, for example, external events that may not be directly relatedto the microblogger such as the local weather, activities of others(e.g., spouse or boss) that may directly or indirectly affect themicroblogger, and so forth.

A second category of things that may be reported or posted throughmicroblog entries include “subjective user states” of the microblogger.Subjective user states of a microblogger include any subjective state orstatus associated with the microblogger that can only be typicallyreported by the microblogger (e.g., generally cannot be reported by athird party or by a device). Such states including, for example, thesubjective mental state of the microblogger (e.g., “I am feelinghappy”), the subjective physical state of the microblogger (e.g., “myankle is sore” or “my ankle does not hurt anymore” or “my vision isblurry”), and the subjective overall state of the microblogger (e.g.,“I'm good” or “I'm well”). Note that the term “subjective overall state”as will be used herein refers to those subjective states that may notfit neatly into the other two categories of subjective user statesdescribed above (e.g., subjective mental states and subjective physicalstates). Although microblogs are being used to provide a wealth ofpersonal information, they have thus far been primarily limited to theiruse as a means for providing commentaries and for maintaining opendiaries.

In accordance with various embodiments, methods, systems, and computerprogram products are provided to, among other things, solicit andacquire at least a portion of objective occurrence data including dataindicating incidence of at least one objective occurrence, thesolicitation being directly or indirectly prompted based, at least inpart on a hypothesis that links one or more subjective user states withone or more objective occurrences and in response to an incidence of atleast one subjective user state associated with a user.

In various embodiments, a “hypothesis” may define one or morerelationships or links between one or more subjective user states andone or more objective occurrences. In some embodiments, a hypothesis maybe defined by a sequential pattern that indicates or suggests a temporalor specific time sequencing relationship between one or more subjectiveuser states and one or more objective occurrences. In some cases, theone or more subjective user states associated with the hypothesis may bebased on past incidences of one or more subjective user states that areassociated with a user, that are associated with multiple users, thatare associated with a sub-group of the general population, or that areassociated with the general population. Similarly, the one or moreobjective occurrences associated with the hypothesis may be based onpast incidences of objective occurrences.

In some cases, a hypothesis may be formulated when it is determined thata particular pattern of events (e.g., incidences of one or moresubjective user states and one or more objective occurrences) occursrepeatedly with respect to a particular user, a group of users, a subsetof the general population, or the general population. For example, ahypothesis may be formulated that suggests or predicts that a personwill likely have an upset stomach after eating a hot fudge sundae whenit is determined that multiple users had reported having an upsetstomach after eating a hot fudge sundae. In other cases, a hypothesismay be formulated based, at least in part, on a single pattern of eventsand historical data related to such events. For instance, a hypothesismay be formulated when a person reports that he had a stomach ache aftereating a hot fudge sundae, and historical data suggests that a segmentof the population may not be able to digest certain nutrients includedin a hot fudge sundae (e.g., the hypothesis would suggest or indicatethat the person may get stomach aches whenever the person eats a hotfudge sundae).

The subjective user state data to be acquired by the methods, systems,and the computer program products may include data indicating theincidence of at least one subjective user state associated with a user.Such subjective user state data together with objective occurrence dataincluding data indicating incidence of at least one objective occurrencemay then be correlated. The results of the correlation may be presentedin a variety of different forms and may, in some cases, confirm theveracity of the hypothesis. The results of the correlation, in variousembodiments, may be presented to the user, to other users, or to one ormore third parties as will be further described herein.

In some embodiments, the correlation of the acquired subjective userstate data with the objective occurrence data may facilitate indetermining a causal relationship between at least one objectiveoccurrence (e.g., cause) and at least one subjective user state (e.g.,result). For example, determining whenever a user eats a banana the useralways or sometimes feels good. Note that an objective occurrence doesnot need to occur prior to a corresponding subjective user state butinstead, may occur subsequent or at least partially concurrently withthe incidence of the subjective user state. For example, a person maybecome “gloomy” (e.g., subjective user state) whenever it is about torain (e.g., objective occurrence) or a person may become gloomy while(e.g., concurrently) it is raining. Further, in some cases, subjectiveuser states may actually be the “cause” while an objective occurrencemay be the “result.” For instance, when a user is angry (e.g.,subjective user state), the user's angry state may cause his bloodpressure (e.g., objective occurrence) to rise. Thus, a more relevantpoint to determine between subjective user states and objectiveoccurrences is whether there are any links or relationships between thetwo types of events (e.g., subjective user states and objectiveoccurrences).

An “objective occurrence data,” as will be described herein, may includedata that indicate incidence of at least one objective occurrence. Insome embodiments, an objective occurrence may be any physicalcharacteristic, event, happenings, or any other aspect that may beassociated with, is of interest to, or may somehow impact a user thatcan be objectively reported by at least a third party or a sensordevice. Note, however, that an objective occurrence does not have to beactually reported by a sensor device or by a third party, but instead,may be reported by the user himself or herself (e.g., via microblogentries). Examples of objectively reported occurrences that could beindicated by the objective occurrence data include, for example, auser's food, medicine, or nutraceutical intake, the user's location atany given point in time, a user's exercise routine, a user'sphysiological characteristics such as blood pressure, social orprofessional activities, the weather at a user's location, activitiesassociated with third parties, occurrence of external events such as theperformance of the stock market, and so forth.

As briefly described earlier, the objective occurrence data to beacquired may include data that indicate the incidence or occurrence ofat least one objective occurrence. In situations where the objectiveoccurrence data to be acquired indicates multiple objective occurrences,each of the objective occurrences indicated by the acquired objectiveoccurrence data may be solicited, while in other embodiments, only oneor a subset of the objective occurrences indicated by the acquiredobjective occurrence data may be solicited.

A “subjective user state,” in contrast, is in reference to anysubjective user state or status associated with a user (e.g., a bloggeror microblogger) at any moment or interval in time that only the usercan typically indicate or describe. Such states include, for example,the subjective mental state of the user (e.g., user is feeling sad), thesubjective physical state (e.g., physical characteristic) of the userthat only the user can typically indicate (e.g., a backache or an easingof a backache as opposed to blood pressure which can be reported by ablood pressure device and/or a third party), and the subjective overallstate of the user (e.g., user is “good”).

Examples of subjective mental states include, for example, happiness,sadness, depression, anger, frustration, elation, fear, alertness,sleepiness, and so forth. Examples of subjective physical statesinclude, for example, the presence, easing, or absence of pain, blurryvision, hearing loss, upset stomach, physical exhaustion, and so forth.Subjective overall states may include any subjective user states thatcannot be easily categorized as a subjective mental state or as asubjective physical state. Examples of subjective overall statesinclude, for example, the user “being good,” “bad,” “exhausted,” “lackof rest,” “wellness,” and so forth.

The term “correlating” as will be used herein may be in reference to adetermination of one or more relationships between at least twovariables. Alternatively, the term “correlating” may merely be inreference to the linking or associating of the at least two variables.In the following exemplary embodiments, the first variable is subjectiveuser state data that indicates at least one subjective user state andthe second variable is objective occurrence data that indicates at leastone objective occurrence. In embodiments where the subjective user statedata indicates multiple subjective user states, each of the subjectiveuser states indicated by the subjective user state data may representdifferent incidences of the same or similar type of subjective userstate (e.g., happiness). Alternatively, the subjective user state datamay indicate multiple subjective user states that represent differentincidences of different types of subjective user states (e.g., happinessand sadness).

Similarly, in some embodiments where the objective occurrence data mayindicate multiple objective occurrences, each of the objectiveoccurrences indicated by the objective occurrence data may representdifferent incidences of the same or similar type of objective occurrence(e.g., exercising). In alternative embodiments, however, each of theobjective occurrences indicated by the objective occurrence data mayrepresent different incidences of different types of objectiveoccurrence (e.g., user exercising and user resting).

Various techniques may be employed for correlating subjective user statedata with objective occurrence data in various alternative embodiments.For example, in some embodiments, the correlation of the objectiveoccurrence data with the subjective user state data may be accomplishedby determining a sequential pattern associated with at least onesubjective user state indicated by the subjective user state data and atleast one objective occurrence indicated by the objective occurrencedata. In other embodiments, the correlation of the objective occurrencedata with the subjective user state data may involve determiningmultiple sequential patterns associated with multiple subjective userstates and multiple objective occurrences.

A sequential pattern, as will be described herein, may define timeand/or temporal relationships between two or more events (e.g., one ormore subjective user states and one or more objective occurrences). Inorder to determine a sequential pattern, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence may be solicited, the solicitation being promptedbased, at least in part, on a hypothesis linking one or more subjectiveuser states with one or more objective occurrences and in response, atleast in part, to an incidence of at least one subjective user stateassociated with a user.

For example, suppose a hypothesis suggests that a user or a group ofusers tend to be depressed whenever the weather is bad (e.g., cloudy orovercast weather). In some implementations, such a hypothesis may havebeen derived based on, for example, reported past events (e.g., reportedpast subjective user states of a user or a group of users and reportedpast objective occurrences). Based at least in part on the hypothesisand upon a user reporting being emotionally depressed, objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence may be solicited from, for example, the user orfrom one or more third party sources such as a weather reportingservice. If the solicitation for the objective occurrence data issuccessful then the objective occurrence data may be acquired from thesource (e.g., a user, one or more third party sources, or one or moresensors). If the acquired objective occurrence data indicates that theweather was indeed bad when the user felt depressed, then this mayconfirm the veracity of the hypothesis. On the other hand, if the datathat is acquired after the solicitation indicates that the weather wasgood when the user was depressed, this may indicate that there is aweaker correlation or link between depression and bad weather.

As briefly described above, a hypothesis may be represented by asequential pattern that may merely indicate or represent the temporalrelationship or relationships between at least one subjective user stateand at least one objective occurrence (e.g., whether the incidence oroccurrence of at least one subjective user state occurred before, after,or at least partially concurrently with the incidence of the at leastone objective occurrence). In alternative implementations, and as willbe further described herein, a sequential pattern may indicate a morespecific time relationship between the incidences of one or moresubjective user states and the incidences of one or more objectiveoccurrences. For example, a sequential pattern may represent thespecific pattern of events (e.g., one or more objective occurrences andone or more subjective user states) that occurs along a timeline.

The following illustrative example is provided to describe how asequential pattern associated with at least one subjective user stateand at least one objective occurrence may be determined based, at leastin part, on the temporal relationship between the incidence of at leastone subjective user state and the incidence of at least one objectiveoccurrence in accordance with some embodiments. For these embodiments,the determination of a sequential pattern may initially involvedetermining whether the incidence of the at least one subjective userstate occurred within some predefined time increment from the incidenceof the one objective occurrence. That is, it may be possible to inferthat those subjective user states that did not occur within a certaintime period from the incidence of an objective occurrence are notrelated or are unlikely related to the incidence of that objectiveoccurrence.

For example, suppose a user during the course of a day eats a banana andalso has a stomach ache sometime during the course of the day. If theconsumption of the banana occurred in the early morning hours but thestomach ache did not occur until late that night, then the stomach achemay be unrelated to the consumption of the banana and may bedisregarded. On the other hand, if the stomach ache had occurred withinsome predefined time increment, such as within 2 hours of consumption ofthe banana, then it may be concluded that there is a link between thestomach ache and the consumption of the banana. If so, a temporalrelationship between the consumption of the banana and the occurrence ofthe stomach ache may be established. Such a temporal relationship may berepresented by a sequential pattern. Such a sequential pattern maysimply indicate that the stomach ache (e.g., a subjective user state)occurred after (rather than before or concurrently) the consumption ofbanana (e.g., an objective occurrence).

Other factors may also be referenced and examined in order to determinea sequential pattern and whether there is a relationship (e.g., causalrelationship) between an incidence of an objective occurrence and anincidence of a subjective user state. These factors may include, forexample, historical data (e.g., historical medical data such as geneticdata or past history of the user or historical data related to thegeneral population regarding, for example, stomach aches and bananas) asbriefly described above.

In some implementations, a sequential pattern may be determined formultiple subjective user states and multiple objective occurrences. Sucha sequential pattern may particularly map the exact temporal or timesequencing of the various events (e.g., subjective user states andobjective occurrences). The determined sequential pattern may then beused to provide useful information to the user and/or third parties.

The following is another illustrative example of how subjective userstate data may be correlated with objective occurrence data bydetermining multiple sequential patterns and comparing the sequentialpatterns with each other. Suppose, for example, a user such as amicroblogger reports that the user ate a banana on a Monday. Theconsumption of the banana, in this example, is a reported incidence of afirst objective occurrence associated with the user. The user thenreports that 15 minutes after eating the banana, the user felt veryhappy. The reporting of the emotional state (e.g., felt very happy) is,in this example, a reported incidence of a first subjective user state.Thus, the reported incidence of the first objective occurrence (e.g.,eating the banana) and the reported incidence of the first subjectiveuser state (user felt very happy) on Monday may be represented by afirst sequential pattern.

On Tuesday, the user reports that the user ate another banana (e.g., asecond objective occurrence associated with the user). The user thenreports that 20 minutes after eating the second banana, the user feltsomewhat happy (e.g., a second subjective user state). Thus, thereported incidence of the second objective occurrence (e.g., eating thesecond banana) and the reported incidence of the second subjective userstate (user felt somewhat happy) on Tuesday may be represented by asecond sequential pattern. Under this scenario, the first sequentialpattern may represent a hypothesis that links feeling happy or veryhappy (e.g., a subjective user state) with eating a banana (e.g., anobjective occurrence). Alternatively, the first sequential pattern maymerely represent historical data (e.g., historical sequential pattern).Note that in this example, the occurrences of the first subjective userstate and the second subjective user state may be indicated bysubjective user state data while the occurrences of the first objectiveoccurrence and the second objective occurrence may be indicated byobjective occurrence data.

In a slight variation of the above example, suppose the user hadforgotten to report the consumption of the second banana on Tuesday butdoes report feeling somewhat happy on Tuesday. This may result in theuser being asked, based at least in part on the reporting of the userfeeling somewhat happy on Tuesday, and based at least in part on thehypothesis, as to whether the user ate anything around the time that theuser felt happy on Tuesday. Upon the user indicating that the user ate abanana on Tuesday, a second sequential pattern may be determined basedon the reported events of Tuesday.

In any event, by comparing the first sequential pattern with the secondsequential pattern, the subjective user state data may be correlatedwith the objective occurrence data. Such a comparison may confirm theveracity of the hypothesis. In some implementations, the comparison ofthe first sequential pattern with the second sequential pattern mayinvolve trying to match the first sequential pattern with the secondsequential pattern by examining certain attributes and/or metrics. Forexample, comparing the first subjective user state (e.g., user felt veryhappy) of the first sequential pattern with the second subjective userstate (e.g., user felt somewhat happy) of the second sequential patternto see if they at least substantially match or are contrasting (e.g.,being very happy in contrast to being slightly happy or being happy incontrast to being sad). Similarly, comparing the first objectiveoccurrence (e.g., eating a banana) of the first sequential pattern maybe compared to the second objective occurrence (e.g., eating of anotherbanana) of the second sequential pattern to determine whether they atleast substantially match or are contrasting.

A comparison may also be made to determine if the extent of timedifference (e.g., 15 minutes) between the first subjective user state(e.g., user being very happy) and the first objective occurrence (e.g.,user eating a banana) matches or are at least similar to the extent oftime difference (e.g., 20 minutes) between the second subjective userstate (e.g., user being somewhat happy) and the second objectiveoccurrence (e.g., user eating another banana). These comparisons may bemade in order to determine whether the first sequential pattern matchesthe second sequential pattern. A match or substantial match wouldsuggest, for example, that a subjective user state (e.g., happiness) islinked to a particular objective occurrence (e.g., consumption ofbanana). In other words, confirming the hypothesis that happiness may belinked to the consumption of bananas.

As briefly described above, the comparison of the first sequentialpattern with the second sequential pattern may include a determinationas to whether, for example, the respective subjective user states andthe respective objective occurrences of the sequential patterns arecontrasting subjective user states and/or contrasting objectiveoccurrences. For example, suppose in the above example the user hadreported that the user had eaten a whole banana on Monday and felt veryenergetic (e.g., first subjective user state) after eating the wholebanana (e.g., first objective occurrence). Suppose that the user alsoreported that on Tuesday he ate a half a banana instead of a wholebanana and only felt slightly energetic (e.g., second subjective userstate) after eating the half banana (e.g., second objective occurrence).In this scenario, the first sequential pattern (e.g., feeling veryenergetic after eating a whole banana) may be compared to the secondsequential pattern (e.g., feeling slightly energetic after eating only ahalf of a banana) to at least determine whether the first subjectiveuser state (e.g., being very energetic) and the second subjective userstate (e.g., being slightly energetic) are contrasting subjective userstates. Another determination may also be made during the comparison todetermine whether the first objective occurrence (eating a whole banana)is in contrast with the second objective occurrence (e.g., eating a halfof a banana).

In doing so, an inference may be made that eating a whole banana insteadof eating only a half of a banana makes the user happier or eating morebanana makes the user happier. Thus, the word “contrasting” as used herewith respect to subjective user states refers to subjective user statesthat are the same type of subjective user states (e.g., the subjectiveuser states being variations of a particular type of subjective userstates such as variations of subjective mental states). Thus, forexample, the first subjective user state and the second subjective userstate in the previous illustrative example are merely variations ofsubjective mental states (e.g., happiness). Similarly, the use of theword “contrasting” as used here with respect to objective occurrencesrefers to objective states that are the same type of objectiveoccurrences (e.g., consumption of food such as banana).

As those skilled in the art will recognize, a stronger correlationbetween the subjective user state data and the objective occurrence datacould be obtained if a greater number of sequential patterns (e.g., ifthere was a third sequential pattern, a fourth sequential pattern, andso forth, that indicated that the user became happy or happier wheneverthe user ate bananas) are used as a basis for the correlation. Note thatfor ease of explanation and illustration, each of the exemplarysequential patterns to be described herein will be depicted as asequential pattern of an incidence of a single subjective user state andan incidence of a single objective occurrence. However, those skilled inthe art will recognize that a sequential pattern, as will be describedherein, may also be associated with incidences or occurrences ofmultiple objective occurrences and/or multiple subjective user states.For example, suppose the user had reported that after eating a banana,he had gulped down a can of soda. The user then reported that he becamehappy but had an upset stomach. In this example, the sequential patternassociated with this scenario will be associated with two objectiveoccurrences (e.g., eating a banana and drinking a can of soda) and twosubjective user states (e.g., user having an upset stomach and feelinghappy).

In some embodiments, and as briefly described earlier, the sequentialpatterns derived from subjective user state data and objectiveoccurrence data may be based on temporal relationships between objectiveoccurrences and subjective user states. For example, whether asubjective user state occurred before, after, or at least partiallyconcurrently with an objective occurrence. For instance, a plurality ofsequential patterns derived from subjective user state data andobjective occurrence data may indicate that a user always has a stomachache (e.g., subjective user state) after eating a banana (e.g., firstobjective occurrence).

FIGS. 1 a and 1 b illustrate an example environment in accordance withvarious embodiments. In the illustrated environment, an exemplary system100 may include at least a computing device 10 (see FIG. 1 b). Thecomputing device 10, which may be a server (e.g., network server) or astandalone device, may be employed in order to, among other things,solicit and acquire at least a portion of objective occurrence data 70*including data indicating occurrence of at least one objectiveoccurrence 71*, to acquire subjective user state data 60* including dataindicating incidence of at least one subjective user state 61*associated with a user 20*, and to correlate the subjective user statedata 60* with the objective occurrence data 70*. In embodiments in whichthe computing device 10 is a server, the exemplary system 100 may alsoinclude a mobile device 30 to at least solicit and acquire at least aportion of the objective occurrence data 70* including the dataindicating incidence of at least one objective occurrence 71* inresponse to, for example, a request made by the computing device 10 forobjective occurrence data 70*. Note that in the following, “*” indicatesa wildcard. Thus, user 20* may indicate a user 20 a or a user 20 b ofFIGS. 1 a and 1 b.

The term “standalone device” as referred to herein may be in referenceto a device or system that is configured to acquire the subjective userstate data 60* and the objective occurrence data 70* and performs acorrelation operation to at least substantially correlate the subjectiveuser state data 60* with the objective occurrence data 70*. In contrast,a mobile device 30, although may acquire both the subjective user statedata 60* and the objective occurrence data 70* like a standalone device,the mobile device 30 does not perform a correlation operation in orderto substantially correlate the subjective user state data 60* with theobjective occurrence data 70*.

As previously indicated, in some embodiments, the computing device 10may be a network server in which case the computing device 10 maycommunicate with a user 20 a via a mobile device 30 and through awireless and/or wired network 40. A network server, as will be describedherein, may be in reference to a server located at a single network siteor located across multiple network sites or a conglomeration of serverslocated at multiple network sites. The mobile device 30 may be a varietyof computing/communication devices including, for example, a cellularphone, a personal digital assistant (PDA), a laptop, a desktop, or othertypes of computing/communication device that can communicate with thecomputing device 10. In some embodiments, the mobile device 30 may be ahandheld device such as a cellular telephone, a smartphone, a MobileInternet Device (MID), an Ultra Mobile Personal Computer (UMPC), aconvergent device such as a personal digital assistant (PDA), and soforth.

In alternative embodiments, the computing device 10 may be a standalonecomputing device 10 (or simply “standalone device”) that communicatesdirectly with a user 20 b. For these embodiments, the computing device10 may be any type of handheld device. In various embodiments, thecomputing device 10 (as well as the mobile device 30) may be apeer-to-peer network component device. In some embodiments, thecomputing device 10 and/or the mobile device 30 may operate via a Web2.0 construct (e.g., Web 2.0 application 268).

In embodiments where the computing device 10 is a server, the computingdevice 10 may acquire the subjective user state data 60* indirectly froma user 20 a via a network interface 120 and via mobile device 30. Inalternative embodiments in which the computing device 10 is a standalonedevice such as a handheld device (e.g., cellular telephone, asmartphone, a MID, a UMPC, a PDA, and so forth), the subjective userstate data 60* may be directly obtained from a user 20 b via a userinterface 122. As will be further described, the computing device 10 maysolicit and acquire at least a portion of the objective occurrence data70* (e.g., objective occurrence data 70 a, objective occurrence data 70b, and/or objective occurrence data 70 c) from one or more alternativesources. For example, in some situations, the computing device 10 mayobtain objective occurrence data 70 a from one or more third partysources 50 (e.g., content providers, other users, health care entities,businesses such as retail businesses, health fitness centers, socialorganizations, and so forth). In some situations, the computing device10 may obtain objective occurrence data 70 b from one or more sensors 35(e.g., blood pressure sensors, glucometers, global positioning system(GPS), heart rate monitor, and so forth). In other situations, thecomputing device 10 (in the case where the computing device 10 is aserver) may obtain objective occurrence data 70 c from a user 20 a viathe mobile device 30 and through the wireless and/or wired network 40 orfrom a user 20 b via user interface 122 (when the computing device 10 isa standalone device).

Note that in embodiments where the computing device 10 is a server, thecomputing device 10 may acquire the objective occurrence data 70 a(e.g., from the one or more third party sources 50) and the objectiveoccurrence data 70 b (e.g. from the one or more sensors 35) via themobile device 30. That is, in certain scenarios, only the user 20 a (andthe mobile device 30) may have access to such data in which case thecomputing device 10 may have to rely on the user 20 a via the mobiledevice 30 in order to acquire the objective occurrence data 70 a and 70b.

In order to acquire the objective occurrence data 70*, the computingdevice 10 may solicit at least a portion of the objective occurrencedata 70* from one or more of the sources (e.g., user 20*, one or morethird party sources 50, and/or one or more remote devices including oneor more sensors 35). For example, in order to solicit at least a portionof the objective occurrence data 70 a including soliciting dataindicating incidence of at least one objective occurrence 71 a, thecomputing device 10 may transmit a solicitation for objective occurrencedata 75 a to the one or more third party sources 50 via wireless and/orwired networks 40. In order to solicit at least a portion of theobjective occurrence data 70 b including soliciting data indicatingincidence of at least one objective occurrence 71 b, the computingdevice 10 may transmit a solicitation for objective occurrence data 75 bto the one or more sensors 35. Finally, in order to solicit at least aportion of the objective occurrence data 70 c including soliciting dataindicating incidence of at least one objective occurrence 71 c, thecomputing device 10 may transmit or indicate a solicitation forobjective occurrence data 75 c to a user 20*.

Note that an objective occurrence data 70* (e.g., objective occurrencedata 70 a, 70 b, or 70 c) may include data that indicates multipleincidences of objective occurrences. For ease of understanding andsimplicity, however, each of the objective occurrence data 70*illustrated in FIG. 1 a have been depicted as including only dataindicating incidence of at least one objective occurrence 71* and dataindicating incidence of at least a second objective occurrence 72*.However, in alternative implementations, each of the objectiveoccurrence data 70* may also include data indicating incidence of atleast a third objective occurrence, data indicating incidence of atleast a fourth objective occurrence, and so forth. In variousimplementations, only a portion of the objective occurrence data 70* mayneed to be solicited. For example, in some implementations, only thedata indicating incidence of at least one objective occurrence 71* maybe solicited while the data indicating incidence of at least a secondobjective occurrence 72* may have be provided without any solicitationof such data.

In various embodiments, and regardless of whether the computing device10 is a server or a standalone device, the computing device 10 may haveaccess to at least one hypothesis 77. For example, in some situations, ahypothesis 77 may have been generated based on reported past eventsincluding past incidences of one or more subjective user states (whichmay be associated with a user 20*, a group of users 20*, a portion ofthe general population, or the general population) and past incidencesof one or more objective occurrences. Such a hypothesis 77, in someinstances, may be stored in a memory 140 to be easily accessible.

For ease of illustration and explanation, the following systems andoperations to be described herein will be generally described in thecontext of the computing device 10 being a network server. However,those skilled in the art will recognize that these systems andoperations may also be implemented when the computing device 10 is astandalone device such as a handheld device that may communicatedirectly with a user 20 b.

The computing device 10, in various implementations, may be configuredto solicit at least a portion of objective occurrence data 70* includingsoliciting data indicating incidence of at least one objectiveoccurrence 71*. The solicitation of the data indicating incidence of atleast one objective occurrence data 71* may be based, at least in part,on a hypothesis 77 that links one or more subjective user states withone or more objective occurrences and in response, at least in part, toan incidence of at least one subjective user state associated with auser 20*. In the case where the computing device 10 is a server, thecomputing device 10, based at least in part, on the hypothesis 77 and inresponse to the incidence of the at least one subjective user stateassociated with a user 20 a, may transmit a solicitation or a requestfor the data indicating incidence of at least one objective occurrence71* to the user 20 a via a mobile device 30, to one or more remotedevices including one or more sensors 35, and/or to one or more thirdparty sources 50. Note that in some situations, the mobile device 30 maybe solicited for the data indicating incidence of at least one objectiveoccurrence 71 c rather than soliciting from the user 20 a. That is, insome situations, the solicited data may already have been provided tothe mobile device 30 by the user 20 a.

In the case where the computing device 10 is a standalone device, thecomputing device 10, may be configured to solicit objective occurrencedata 70* including soliciting data indicating incidence of at least oneobjective occurrence 70 c directly from a user 20 b via a user interface122, from one or more remote devices (e.g., one or more remote networkservers or one or more sensors 35), and/or from one or more third partysources 50 via at least one of a wireless or wired network 40. Aftersoliciting for the data indicating incidence of at least one objectiveoccurrence 71*, the computing device 10 (e.g., either in the case wherethe computing device 10 is a server or in the case where the computingdevice 10 is a standalone device) may be further designed to acquire thedata indicating incidence of at least one objective occurrence 71* aswell as to acquire other data indicating other incidences of objectiveoccurrences (e.g., data indicating incidence of at least a secondobjective occurrence 72*, and so forth). Examples of the types ofobjective occurrences that may be indicated by the objective occurrencedata 70* include, for example, ingestions of food items, medicines, ornutraceutical by a user 20*, exercise routines executed a user 20*,social or recreational activities of a user 20*, activities performed bythird parties, geographical locations of a user 20*, external events,physical characteristics of a user 20* at any given moment in time, andso forth.

In some embodiments, the computing device 10 may be configured toacquire subjective user state data 60* including data indicatingincidence of at least one subjective user state 61* associated with auser 20*. For example, in embodiments where the computing device 10 is aserver, the computing device 10 may acquire subjective user state data60 a including data indicating incidence of at least one subjective userstate 61 a associated with a user 20 a. Such data may be acquired fromthe user 20 a via a mobile device 30 or from other sources such as othernetwork servers that may have previously stored such data and through atleast one of a wireless network or a wired network 40. In embodimentswhere the computing device 10 is a standalone device, the computingdevice 10 may acquire subjective user state data 60 b including dataindicating incidence of at least one subjective user state 61 bassociated with a user 20 b. Such data may be acquired from the user 20b via a user interface 122.

Note that in various alternative implementations, the subjective userstate data 60* may include data that indicates multiple subjective userstates associated with a user 20*. For ease of illustration andexplanation, each of the subjective user state data 60 a and thesubjective user state data 60 b illustrated in FIGS. 1 a and 1 b havebeen depicted as having only data indicating incidence of at least onesubjective user state 61* (e.g., 61 a or 61 b) and data indicatingincidence of at least a second subjective user state 62* (e.g., 62 a or62 b). However, in alternate implementations, the subjective user statedata 60* may further include data indicating incidences of at least athird, a fourth, a fifth, and so forth, subjective user statesassociated with a user 20*.

Examples of subjective user states that may be indicated by thesubjective user state data 60* include, for example, subjective mentalstates of a user 20* (e.g., user 20* is sad or angry), subjectivephysical states of the user 20* (e.g., physical or physiologicalcharacteristic of the user 20* such as the presence, absence, elevating,or easing of a pain), subjective overall states of the user 20* (e.g.,user 20* is “well”), and/or other subjective user states that only theuser 20* can typically indicate.

The one or more sensors 35 illustrated in FIG. 1 a may be designed forsensing or monitoring various aspects associated with the user 20 a (oruser 20 b). For example, in some implementations, the one or moresensors 35 may include a global positioning system (GPS) device fordetermining the one or more locations of the user 20 a and/or a physicalactivity sensor for measuring physical activities of the user 20 a.Examples of a physical activity sensor include, for example, a pedometerfor measuring physical activities of the user 20 a. In certainimplementations, the one or more sensors 35 may include one or morephysiological sensor devices for measuring physiological characteristicsof the user 20 a. Examples of physiological sensor devices include, forexample, a blood pressure monitor, a heart rate monitor, a glucometer,and so forth. In some implementations, the one or more sensors 35 mayinclude one or more image capturing devices such as a video or digitalcamera.

In some embodiments, objective occurrence data 70 c that may be acquiredfrom a user 20 a via the mobile device 30 (or from user 20 b via userinterface 122) may be acquired in various forms. For these embodiments,the objective occurrence data 70 c may be in the form of blog entries(e.g., microblog entries), status reports, or other types of electronicentries (e.g., diary or calendar entries) or messages. In variousimplementations, the objective occurrence data 70 c acquired from a user20* may indicate, for example, activities (e.g., exercise or food ormedicine intake) performed by the user 20*, certain physicalcharacteristics (e.g., blood pressure or location) associated with theuser 20*, or other aspects associated with the user 20* that the user20* can report objectively. The objective occurrence data 70 c may be inthe form of a text data, audio or voice data, or image data.

In various embodiments, after acquiring the subjective user state data60* including data indicating incidence of at least one subjective userstate 61* and the objective occurrence data 70* including dataindicating incidence of at least one objective occurrence 71*, thecomputing device 10 may be configured to correlate the acquiredsubjective user state data 60* with the acquired objective occurrencedata 70* by, for example, determining whether there is a sequentialrelationship between the one or more subjective user states as indicatedby the acquired subjective user state data 60* and the one or moreobjective occurrences indicated by the acquired objective occurrencedata 70*.

In some embodiments, and as will be further explained in the operationsand processes to be described herein, the computing device 10 may befurther configured to present one or more results of the correlation. Invarious embodiments, the one or more correlation results 80 may bepresented to a user 20* and/or to one or more third parties in variousforms (e.g., in the form of an advisory, a warning, a prediction, and soforth). The one or more third parties may be other users 20* (e.g.,microbloggers), health care providers, advertisers, and/or contentproviders.

As illustrated in FIG. 1 b, computing device 10 may include one or morecomponents and/or sub-modules. As those skilled in the art willrecognize, these components and sub-modules may be implemented byemploying hardware (e.g., in the form of circuitry such as applicationspecific integrated circuit or ASIC, field programmable gate array orFPGA, or other types of circuitry), software, a combination of bothhardware and software, or a general purpose computing device executinginstructions included in a signal-bearing medium. In variousembodiments, computing device 10 may include an objective occurrencedata solicitation module 101, a subjective user state data acquisitionmodule 102, an objective occurrence data acquisition module 104, acorrelation module 106, a presentation module 108, a network interface120 (e.g., network interface card or NIC), a user interface 122 (e.g., adisplay monitor, a touchscreen, a keypad or keyboard, a mouse, an audiosystem including a microphone and/or speakers, an image capturing systemincluding digital and/or video camera, and/or other types of interfacedevices), one or more applications 126 (e.g., a web 2.0 application, avoice recognition application, and/or other applications), and/or memory140, which may include at least one hypothesis 77 and historical data78.

FIG. 2 a illustrates particular implementations of the objectiveoccurrence data solicitation module 101 of the computing device 10 ofFIG. 1 b. The objective occurrence data solicitation module 101 may beconfigured to solicit at least a portion of objective occurrence data70* including soliciting data indicating incidence of at least oneobjective occurrence 71*. In various implementations, the solicitationof the data indicating incidence of at least one objective occurrence71* by the objective occurrence data solicitation module 101 may beprompted based, at least in part, on a hypothesis 77 that links one ormore objective occurrences with one or more subjective user states andin response, at least in part, to incidence of at least one subjectiveuser state associated with a user 20*. For example, if an occurrence orincidence of a subjective user state (e.g., a hangover by a user 20*)has been reported, and if the hypothesis 77 links the same type ofsubjective user state (e.g., a hangover) to an objective occurrence(e.g., consumption of alcohol), then the solicitation of the dataindicating incidence of at least one objective occurrence 71* may be tosolicit data that would indicate an objective occurrence associated withthe user 20* (e.g., consumption of alcohol) that occurred prior to thereported hangover by the user 20*.

The objective occurrence data solicitation module 101 may include one ormore sub-modules in various alternative implementations. For example, invarious implementations, the objective occurrence data solicitationmodule 101 may include a requesting module 202 configured to request forat least a portion of objective occurrence data 70* including requestingfor data indicating incidence of at least one objective occurrence 71*.The requesting module 202 may further include one or more sub-modules.For example, in some implementations, such as when the computing device10 is a standalone device, the requesting module 202 may include a userinterface requesting module 204 configured to request for dataindicating incidence of at least one objective occurrence 71* via a userinterface 122. The user interface requesting module 204, in some cases,may further include a request indication module 205 configured toindicate a request for data indicating incidence of at least oneobjective occurrence 71* via the user interface 122 (e.g., indicatingthrough at least a display system including a display monitor ortouchscreen, or indicating via an audio system including a speaker).

In some implementations, such as when the computing device 10 is aserver, the requesting module 202 may include a network interfacerequesting module 206 configured to request for at least data indicatingincidence of at least one objective occurrence 71* via a networkinterface 120. The requesting module 202 may include other sub-modulesin various alternative implementations. For example, in someimplementations, the requesting module 202 may include a requesttransmission module 207 configured to transmit a request to be providedwith at least data indicating incidence of at least one objectiveoccurrence 71*. Alternatively or in the same implementations, therequesting module 202 may include a request access module 208 configuredto transmit a request to have access to at least data indicatingincidence of at least one objective occurrence 71*.

In the same or different implementations, the network interfacerequesting module 206 may include a configuration module 209 designed toconfigure (e.g., remotely configure) one or more remote devices (e.g., aremote network server, a mobile device 30, or some other network device)to provide at least data indicating incidence of at least one objectiveoccurrence 71*. In the same or different implementations, the requestingmodule 202 may include a directing/instructing module 210 configured todirect or instruct a remote device (e.g., transmitting directions orinstructions to the remote device such as a remote network server or themobile device 30) to provide at least data indicating incidence of atleast one objective occurrence 71*.

The requesting module 202 may include other sub-modules in variousalternative implementations. These sub-modules may be included with therequesting module 202 regardless of whether the computing device 10 is aserver or a standalone device. For example, in some implementations, therequesting module 202 may include a motivation provision module 212configured to provide, among other things, a motivation for requestingfor the data indicating incidence of at least one objective occurrence71*. In the same or different implementations, the requesting module 202may include a selection request module 214 configured to, among otherthings, request a user 20* for a selection of an objective occurrencefrom a plurality of indicated alternative objective occurrences (e.g.,asking the user 20* through the user interface 122* to select fromalternative choices of “bad weather,” “good weather,” “consumedalcohol,” “jogging for one hour,” and so forth).

In the same or different implementations, the requesting module 202 mayinclude a confirmation request module 216 configured to requestconfirmation of an incidence of at least one objective occurrence (e.g.,asking a user 20* through the user interface 122* whether the user 20*ate spicy foods for dinner). In the same or different implementations,the requesting module 202 may include a time/temporal element requestmodule 218 configured to, among other things, request for an indicationof a time or temporal element associated with an incidence of at leastone objective occurrence (e.g., asking the user 20* via the userinterface 122* whether the user 20* ate lunch before, after, or duringwhen the user 20* felt tired?).

In various implementations, the objective occurrence data solicitationmodule 101 of FIG. 2 a may include a hypothesis referencing module 220configured to, among other things, reference at least one hypothesis 77,which in some cases, may be stored in memory 140.

FIG. 2 b illustrates particular implementations of the subjective userstate data acquisition module 102 of the computing device 10 of FIG. 1b. In brief, the subjective user state data acquisition module 102 maybe designed to, among other things, acquire subjective user state data60* including data indicating at least one subjective user state 61*associated with a user 20*. In various embodiments, the subjective userstate data acquisition module 102 may be further designed to acquiredata indicating at least a second subjective user state 62* associatedwith the user 20*, data indicating at least a third subjective userstate associated with the user 20*, and so forth. In some embodiments,the subjective user state data acquisition module 102 may include asubjective user state data reception module 224 configured to receivethe subjective user state data 60* including the data indicatingincidence of the at least one subjective user state 61* associated withthe user 20*, the data indicating incidence of the at least a secondsubjective user state 62* associated with the user 20*, and so forth. Insome implementations, the subjective user state data reception module224 may further include a user interface reception module 226 configuredto receive, via a user interface 122, subjective user state data 60*including at least the data indicating incidence of at least onesubjective user state 61* associated with a user 20*. In the same ordifferent implementations, the subjective user state data receptionmodule 224 may include a network interface reception module 227configured to receive, via a network interface 120, subjective userstate data 60* including at least the data indicating incidence of atleast one subjective user state 61* associated with a user 20*.

The subjective user state data acquisition module 102, in variousimplementations, may include a time data acquisition module 228configured to acquire (e.g., receive or generate) time and/or temporalelements associated with one or more objective occurrences. In someimplementations, the time data acquisition module 228 may include a timestamp acquisition module 230 for acquiring (e.g., acquiring either byreceiving or by generating) one or more time stamps associated with oneor more objective occurrences In the same or different implementations,the time data acquisition module 228 may include a time intervalacquisition module 231 for acquiring (e.g., either by receiving orgenerating) indications of one or more time intervals associated withone or more objective occurrences.

FIG. 2 c illustrates particular implementations of the objectiveoccurrence data acquisition module 104 of the computing device 10 ofFIG. 1 b. In brief, the objective occurrence data acquisition module 104may be configured to, among other things, acquire objective occurrencedata 70* including data indicating incidence of at least one objectiveoccurrence 71*, data indicating incidence of at least a second objectiveoccurrence 72*, and so forth. As further illustrated, in someimplementations, the objective occurrence data acquisition module 104may include an objective occurrence data reception module 234 configuredto, among other things, receive objective occurrence data 70* from auser 20*, from one or more third party sources 50 (e.g., one or morethird parties), or from one or more remote devices such as one or moresensors 35 or one or more remote network servers.

The objective occurrence data reception module 234, in turn, may furtherinclude one or more sub-modules. For example, in some implementations,such as when the computing device 10 is a standalone device, theobjective occurrence data reception module 234 may include a userinterface data reception module 235 configured to receive objectiveoccurrence data 70* via a user interface 122 (e.g., a keyboard, a mouse,a touchscreen, a microphone, an image capturing device such as a digitalcamera, and so forth). In some cases, the objective occurrence data 70*(e.g., objective occurrence data 70 c) to be received via the userinterface 122 may have been provided by and originate from a user 20 b.In other cases, the objective occurrence data 70* to be received via theuser interface 122 may have originated from one or more third partysources 50 or from one or more remote sensors 35 and provided by user 20b. In some implementations, such as when the computing device 10 is aserver, the objective occurrence data reception module 234 may include anetwork interface data reception module 236 configured to, among otherthings, receive objective occurrence data 70* from at least one of awireless network or a wired network 40. The network interface datareception module 236 may directly or indirectly receive the objectiveoccurrence data 70* from a user 20 a, from one or more third partysources 50, or from one or more remote devices such as one or moresensors 35.

Turning now to FIG. 2 d illustrating particular implementations of thecorrelation module 106 of the computing device 10 of FIG. 1 b. Thecorrelation module 106 may be configured to, among other things,correlate subjective user state data 60* with objective occurrence data70* based, at least in part, on a determination of at least onesequential pattern of at least one objective occurrence and at least onesubjective user state. In various embodiments, the correlation module106 may include a sequential pattern determination module 242 configuredto determine one or more sequential patterns of one or more incidencesof subjective user states and one or more incidences of objectiveoccurrences.

The sequential pattern determination module 242, in variousimplementations, may include one or more sub-modules that may facilitatein the determination of one or more sequential patterns. As depicted,the one or more sub-modules that may be included in the sequentialpattern determination module 242 may include, for example, a “withinpredefined time increment determination” module 244, a temporalrelationship determination module 246, a subjective user state andobjective occurrence time difference determination module 245, and/or ahistorical data referencing module 243. In brief, the within predefinedtime increment determination module 244 may be configured to determinewhether an incidence of at least one subjective user state associatedwith a user 20* occurred within a predefined time increment from anincidence of at least one objective occurrence. For example, determiningwhether a user 20* “feeling bad” (i.e., a subjective user state)occurred within ten hours (i.e., predefined time increment) of eating alarge chocolate sundae (i.e., an objective occurrence). Such a processmay be used in order to filter out events that are likely not related orto facilitate in determining the strength of correlation betweensubjective user state data 60* and objective occurrence data 70*. Forexample, if the user 20* “feeling bad” occurred more than 10 hours aftereating the chocolate sundae, then this may indicate a weaker correlationbetween a subjective user state (e.g., feeling bad) and an objectiveoccurrence (e.g., eating a chocolate sundae).

The temporal relationship determination module 246 of the sequentialpattern determination module 242 may be configured to determine thetemporal relationships between one or more incidences of subjective userstates associated with a user 20* and one or more incidences ofobjective occurrences. For example, this determination may entaildetermining whether an incidence of a particular subjective user state(e.g., sore back) occurred before, after, or at least partiallyconcurrently with an incidence of a particular objective occurrence(e.g., sub-freezing temperature).

The subjective user state and objective occurrence time differencedetermination module 245 of the sequential pattern determination module242 may be configured to determine the extent of time difference betweenan incidence of at least one subjective user state associated with auser 20* and an incidence of at least one objective occurrence. Forexample, determining how long after taking a particular brand ofmedication (e.g., objective occurrence) did a user 20* feel “good”(e.g., subjective user state).

The historical data referencing module 243 of the sequential patterndetermination module 242 may be configured to reference historical data78 in order to facilitate in determining sequential patterns. Forexample, in various implementations, the historical data 78 that may bereferenced may include, for example, general population trends (e.g.,people having a tendency to have a hangover after drinking or ibuprofenbeing more effective than aspirin for toothaches in the generalpopulation), medical information such as genetic, metabolome, orproteome information related to the user 20* (e.g., genetic informationof the user 20* indicating that the user 20* is susceptible to aparticular subjective user state in response to occurrence of aparticular objective occurrence), or historical sequential patterns suchas known sequential patterns of the general population or of the user20* (e.g., people tending to have difficulty sleeping within five hoursafter consumption of coffee). In some instances, such historical data 78may be useful in associating one or more incidences of subjective userstates associated with a user 20* with one or more incidences ofobjective occurrences.

In some embodiments, the correlation module 106 may include a sequentialpattern comparison module 248. As will be further described herein, thesequential pattern comparison module 248 may be configured to comparetwo or more sequential patterns with respect to each other to determine,for example, whether the sequential patterns at least substantiallymatch each other or to determine whether the sequential patterns arecontrasting sequential patterns.

As depicted in FIG. 2d, in various implementations, the sequentialpattern comparison module 248 may further include one or moresub-modules that may be employed in order to, for example, facilitate inthe comparison of different sequential patterns. For example, in variousimplementations, the sequential pattern comparison module 248 mayinclude one or more of a subjective user state equivalence determinationmodule 250, an objective occurrence equivalence determination module251, a subjective user state contrast determination module 252, anobjective occurrence contrast determination module 253, a temporalrelationship comparison module 254, and/or an extent of time differencecomparison module 255. In some implementations, the sequential patterncomparison module 248 may be employed in order to, for example, confirmthe veracity of a hypothesis 77.

The subjective user state equivalence determination module 250 of thesequential pattern comparison module 248 may be configured to determinewhether subjective user states associated with different sequentialpatterns are at least substantially equivalent. For example, thesubjective user state equivalence determination module 250 may determinewhether a first subjective user state of a first sequential pattern isequivalent to a second subjective user state of a second sequentialpattern. For instance, suppose a user 20* reports that on Monday he hada stomach ache (e.g., first subjective user state) after eating at aparticular restaurant (e.g., a first objective occurrence), and supposefurther that the user 20* again reports having a stomach ache (e.g., asecond subjective user state) after eating at the same restaurant (e.g.,a second objective occurrence) on Tuesday, then the subjective userstate equivalence determination module 250 may be employed in order tocompare the first subjective user state (e.g., stomach ache) with thesecond subjective user state (e.g., stomach ache) to determine whetherthey are equivalent. Note that in this example, the first sequentialpattern may represent a hypothesis 77 linking a subjective user state(e.g., stomach ache) to an objective occurrence (e.g., eating at aparticular restaurant).

In contrast, the objective occurrence equivalence determination module251 of the sequential pattern comparison module 248 may be configured todetermine whether objective occurrences of different sequential patternsare at least substantially equivalent. For example, the objectiveoccurrence equivalence determination module 251 may determine whether afirst objective occurrence of a first sequential pattern is equivalentto a second objective occurrence of a second sequential pattern. Forinstance, in the above example, the objective occurrence equivalencedetermination module 251 may compare eating at the particular restauranton Monday (e.g., first objective occurrence) with eating at the samerestaurant on Tuesday (e.g., second objective occurrence) in order todetermine whether the first objective occurrence is equivalent to thesecond objective occurrence.

In some implementations, the sequential pattern comparison module 248may include a subjective user state contrast determination module 252that may be configured to determine whether subjective user statesassociated with different sequential patterns are contrasting subjectiveuser states. For example, the subjective user state contrastdetermination module 252 may determine whether a first subjective userstate of a first sequential pattern is a contrasting subjective userstate from a second subjective user state of a second sequentialpattern. To illustrate, suppose a user 20* reports that he felt very“good” (e.g., first subjective user state) after jogging for an hour(e.g., first objective occurrence) on Monday, but reports that he felt“bad” (e.g., second subjective user state) when he did not exercise(e.g., second objective occurrence) on Tuesday, then the subjective userstate contrast determination module 252 may compare the first subjectiveuser state (e.g., feeling good) with the second subjective user state(e.g., feeling bad) to determine that they are contrasting subjectiveuser states.

In some implementations, the sequential pattern comparison module 248may include an objective occurrence contrast determination module 253that may be configured to determine whether objective occurrences ofdifferent sequential patterns are contrasting objective occurrences. Forexample, the objective occurrence contrast determination module 253 maydetermine whether a first objective occurrence of a first sequentialpattern is a contrasting objective occurrence from a second objectiveoccurrence of a second sequential pattern. For instance, in the previousexample, the objective occurrence contrast determination module 253 maycompare the “jogging” on Monday (e.g., first objective occurrence) withthe “no jogging” on Tuesday (e.g., second objective occurrence) in orderto determine whether the first objective occurrence is a contrastingobjective occurrence from the second objective occurrence. Based on thecontrast determination, an inference may be made that the user 20* mayfeel better by jogging rather than by not jogging at all.

In some embodiments, the sequential pattern comparison module 248 mayinclude a temporal relationship comparison module 254 that may beconfigured to make comparisons between different temporal relationshipsof different sequential patterns. For example, the temporal relationshipcomparison module 254 may compare a first temporal relationship betweena first subjective user state and a first objective occurrence of afirst sequential pattern with a second temporal relationship between asecond subjective user state and a second objective occurrence of asecond sequential pattern in order to determine whether the firsttemporal relationship at least substantially matches the second temporalrelationship.

For example, referring back to the earlier restaurant example, supposethe user 20* eating at the particular restaurant (e.g., first objectiveoccurrence) and the subsequent stomach ache (e.g., first subjective userstate) on Monday represents a first sequential pattern while the user20* eating at the same restaurant (e.g., second objective occurrence)and the subsequent stomach ache (e.g., second subjective user state) onTuesday represents a second sequential pattern. In this example, theoccurrence of the stomach ache after (rather than before orconcurrently) eating at the particular restaurant on Monday represents afirst temporal relationship associated with the first sequential patternwhile the occurrence of a second stomach ache after (rather than beforeor concurrently) eating at the same restaurant on Tuesday represents asecond temporal relationship associated with the second sequentialpattern.

Under such circumstances, the temporal relationship comparison module254 may compare the first temporal relationship to the second temporalrelationship in order to determine whether the first temporalrelationship and the second temporal relationship at least substantiallymatch (e.g., stomach aches in both temporal relationships occurringafter eating at the restaurant). Such a match may result in theinference that a stomach ache is associated with eating at theparticular restaurant and may, in some instances, confirm the veracityof a hypothesis 77.

In some implementations, the sequential pattern comparison module 248may include an extent of time difference comparison module 255 that maybe configured to compare the extent of time differences betweenincidences of subjective user states and incidences of objectiveoccurrences of different sequential patterns. For example, the extent oftime difference comparison module 255 may compare the extent of timedifference between incidence of a first subjective user state andincidence of a first objective occurrence of a first sequential patternwith the extent of time difference between incidence of a secondsubjective user state and incidence of a second objective occurrence ofa second sequential pattern. In some implementations, the comparisonsmay be made in order to determine that the extent of time differences ofthe different sequential patterns at least substantially or proximatelymatch.

In some embodiments, the correlation module 106 may include a strengthof correlation determination module 256 for determining a strength ofcorrelation between subjective user state data 60* and objectiveoccurrence data 70*. In some implementations, the strength ofcorrelation may be determined based, at least in part, on the resultsprovided by the other sub-modules of the correlation module 106 (e.g.,the sequential pattern determination module 242, the sequential patterncomparison module 248, and their sub-modules).

FIG. 2 e illustrates particular implementations of the presentationmodule 108 of the computing device 10 of FIG. 1 b. In variousimplementations, the presentation module 108 may be configured topresent, for example, one or more results of the correlation operationsperformed by the correlation module 106. In some implementations, thepresentation module 108 may include a network interface transmissionmodule 258 configured to transmit one or more results of a correlationoperation performed by the correlation module 106 via a networkinterface 120 (e.g., NIC). In the same or different implementations, thepresentation module 108 may include a user interface indication module259 configured to indicate one or more results of a correlationoperation performed by the correlation module 106 via a user interface122 (e.g., display monitor or audio system including a speaker).

The presentation module 108 may be particularly designed to present oneor more results of a correlation operation performed by the correlationmodule 106 in a variety of different forms in various alternativeembodiments. For example, in some implementations, the presentation ofthe one or more results may entail the presentation module 108presenting to the user 20* (or some other third party) an indication ofa sequential relationship between a subjective user state and anobjective occurrence associated with the user 20* (e.g., “whenever youeat a banana, you have a stomach ache”). In alternative implementations,other ways of presenting the results of the correlation may be employed.For example, in various alternative implementations, a notification maybe provided to notify past tendencies or patterns associated with a user20*. In some implementations, a notification of a possible futureoutcome may be provided. In other implementations, a recommendation fora future course of action based on past patterns may be provided. Theseand other ways of presenting the correlation results will be describedin the processes and operations to be described herein.

In order to present the one or more results of a correlation operationperformed by the correlation module 106, the presentation module 108 mayinclude one or more sub-modules. For example, in some implementations,the presentation module 108 may include a sequential relationshippresentation module 260 configured to present an indication of asequential relationship between at least one subjective user state of auser 20* and at least one objective occurrence. In the same or differentimplementations, the presentation module 108 may include a predictionpresentation module 261 configured to present a prediction of a futuresubjective user state of a user 20* resulting from a future objectiveoccurrence associated with the user 20*. In the same or differentimplementations, the prediction presentation module 261 may also bedesigned to present a prediction of a future subjective user state of auser 20* resulting from a past objective occurrence associated with theuser 20*. In some implementations, the presentation module 108 mayinclude a past presentation module 262 that is designed to present apast subjective user state of a user 20* in connection with a pastobjective occurrence associated with the user 20*.

In some implementations, the presentation module 108 may include arecommendation module 263 configured to present a recommendation for afuture action based, at least in part, on the results of a correlationof subjective user state data 60* with objective occurrence data 70* asperformed by the correlation module 106. In certain implementations, therecommendation module 263 may further include a justification module 264for presenting a justification for the recommendation presented by therecommendation module 263. In some implementations, the presentationmodule 108 may include a strength of correlation presentation module 266for presenting an indication of a strength of correlation betweensubjective user state data 60* and objective occurrence data 70*.

In various embodiments, the computing device 10 of FIG. 1 b may includea network interface 120 that may facilitate in communicating with a user20 a, with one or more sensors 35, and/or with one or more third partysources 50 via a wireless and/or wired network 40. For example, inembodiments where the computing device 10 is a server, the computingdevice 10 may include a network interface 120 that may be configured toreceive from the user 20 a subjective user state data 60 a. In someembodiments, objective occurrence data 70 a, 70 b, and/or 70 c may alsobe received through the network interface 120. Examples of a networkinterface 120 includes, for example, a network interface card (NIC) orother devices or systems for communicating through at least one of awireless network or wired network 40.

The computing device 10 may also include a memory 140 for storingvarious data. For example, in some embodiments, memory 140 may beemployed in order to store a hypothesis 77 and/or historical data 78. Insome implementations, the historical data 78 may include historicalsubjective user state data of a user 20* that may indicate one or morepast subjective user states of the user 20*, and historical objectiveoccurrence data that may indicate one or more past objectiveoccurrences. In the same or different implementations, the historicaldata 78 may include historical medical data of a user 20* (e.g.,genetic, metoblome, proteome information), population trends, historicalsequential patterns derived from general population, and so forth.Examples of a memory 140 include, for example, a mass storage device,read only memory (ROM), programmable read only memory (PROM), erasableprogrammable read-only memory (EPROM), random access memory (RAM), flashmemory, synchronous random access memory (SRAM), dynamic random accessmemory (DRAM), and so forth.

In various embodiments, the computing device 10 may include a userinterface 122 to communicate directly with a user 20 b. For example, inembodiments in which the computing device 10 is a standalone device suchas a handheld device (e.g., cellular telephone, smartphone, PDA, and soforth), the user interface 122 may be configured to directly receivefrom the user 20 b subjective user state data 60* and/or objectiveoccurrence data 70*. In some implementations, the user interface 122 mayalso be designed to visually or audioally present the results ofcorrelating subjective user state data 60* with objective occurrencedata 70*. The user interface 122 may include, for example, one or moreof a display monitor, a touch screen, a key board, a key pad, a mouse,an audio system including a microphone and/or one or more speakers, animaging system including a digital or video camera, and/or other userinterface devices.

FIG. 2 f illustrates particular implementations of the one or moreapplications 126 of FIG. 1 b. For these implementations, the one or moreapplications 126 may include, for example, one or more communicationapplications 269 such as a text messaging application and/or an audiomessaging application including a voice recognition system application.In some implementations, the one or more applications 126 may include aweb 2.0 application 268 to facilitate communication via, for example,the World Wide Web.

The various features and characteristics of the components, modules, andsub-modules of the computing device 10 presented thus far will bedescribed in greater detail with respect to the processes and operationsto be described herein. Note that the subjective user state data 60* maybe in a variety of forms including, for example, text messages (e.g.,blog entries, microblog entries, instant messages, text email messages,and so forth), audio messages, and/or images (e.g., an image capturinguser's facial expression or gestures).

Referring to FIG. 2 g illustrating particular implementations of themobile device 30 of FIG. 1 a. The mobile device 30 includes some modulesthat are the same as some of the modules that may be included in thecomputing device 10. These components may have the same features andperform the same or similar types of functions as those of theircorresponding counterparts in the computing device 10. For example, andjust like the computing device 10, the mobile device 30 may include anobjective occurrence data solicitation module 101′, a subjective userstate data acquisition module 102′, an objective occurrence dataacquisition module 104′, a presentation module 108′, a network interface120′, a user interface 122′, one or more application [s] 126′ (e.g.,including a Web 2.0 application), and/or memory 140′ (includinghistorical data 78′).

In various implementations, in addition to these components, the mobiledevice 30 may include an objective occurrence data transmission module160 that is configured to transmit (e.g., transmit via a wireless and/orwired network 40) at least a portion of objective occurrence data 70*including data indicating incidence of at least one objective occurrence71*. In some implementations, the subjective user state data 60 a and/orat least a portion of the objective occurrence data 70* may betransmitted to a network server such as computing device 10. In the sameor different implementations, the mobile device 30 may include acorrelation results reception module 162 that may be configured toreceive, via a wireless and/or wired network 40, results of correlationof subjective user state data 60* with objective occurrence data 70*. Insome implementations, such a correlation may have been performed at anetwork server (e.g., computing device 10).

FIG. 2 h illustrates particular implementations of the objectiveoccurrence data solicitation module 101′ of the mobile device 30 of FIG.2 g. As depicted, the objective occurrence data solicitation module 101′may include some components that are the same or similar to some of thecomponents that may be included in the objective occurrence datasolicitation module 101 of the computing device 10 as illustrated inFIG. 2 a. For example, the objective occurrence data solicitation module101′ may include a requesting module 202′ that further includes a userinterface requesting module 204′ (and a request indication module 205′included with the user interface requesting module 204′), a networkinterface requesting module 206′, a request transmission module 207′, arequest access module 208′, a configuration module 209′, adirecting/instructing module 210′, a motivation provision module 212′, aselection request module 214′, a confirmation request module 216′ and atime/temporal element request module 218′. As will be further describedherein, these components may have the same features and perform the samefunctions as their counterparts in the computing device 10.

In addition, and unlike the computing device 10, the objectiveoccurrence data solicitation module 101′ of the mobile device 30 mayinclude a request to solicit reception module 270 that may be configuredto receive a request to solicit data indicating incidence of at leastone objective occurrence 71*. Such a request, in some implementations,may be remotely generated (e.g. remotely generated at the computingdevice 10) based, at least in part, on a hypothesis 77 and, in somecases, in response, at least in part, to an incidence of at least oneobjective occurrence.

FIG. 2 i illustrates particular implementations of the subjective userstate data acquisition module 102′ of the mobile device 30 of FIG. 2 g.The subjective user state data acquisition module 102′ may include somecomponents that are the same or similar to some of the components thatmay be included in the subjective user state data acquisition module 102(see FIG. 2 b) of the computing device 10. These components may performthe same or similar functions as their counterparts in the subjectiveuser state data acquisition module 102 of the computing device 10. Forexample, the subjective user state data acquisition module 102′ mayinclude a subjective user state data reception module 224′ and a timedata acquisition module 228′. Similar to their counterparts in thecomputing device 10 and performing similar roles, the subjective userstate data reception module 224′ may include a user interface receptionmodule 226′ while the time data acquisition module 228′ may include atime stamp acquisition module 230′ and a time interval acquisitionmodule 231′.

Referring to FIG. 2 j illustrating particular implementations of theobjective occurrence data acquisition module 104′ of the mobile device30 of FIG. 2 g. The objective occurrence data acquisition module 104′may include the same or similar type of components that may be includedin the objective occurrence data acquisition module 104 (see FIG. 2 c)of the computing device 10. For example, the objective occurrence dataacquisition module 104′ may include an objective occurrence datareception module 234′ (which may further include a user interface datareception module 235′ and/or a network interface data reception module236′).

FIG. 2 k illustrates particular implementations of the presentationmodule 108′ of the mobile device 30 of FIG. 2 g. In variousimplementations, the presentation module 108′ may include some of thesame components that may be included in the presentation module 108 (seeFIG. 2 e) of the computing device 10. For example, the presentationmodule 108′ may include a user interface indication module 259′, asequential relationship presentation module 260′, a predictionpresentation module 261′, a past presentation module 262′, arecommendation module 263′ (which may further include a justificationmodule 264′), and/or a strength of correlation presentation module 266′.

FIG. 2 l illustrates particular implementations of the one or moreapplications 126′ of the mobile device 30 of FIG. 2 g. In variousimplementations, the one or more applications 126′ may include the sameor similar applications included in the one or more applications 126 ofthe computing device 10 (see FIG. 2 f). For example, the one or moreapplications 126′ may include one or more communication applications269′ and a web 2.0 application 268′ performing similar functions astheir counterparts in the computing device 10.

A more detailed discussion of these components (e.g., modules andinterfaces) that may be included in the mobile device 30 and those thatmay be included in the computing device 10 will be provided with respectto the processes and operations to be described herein.

FIG. 3 illustrates an operational flow 300 representing exampleoperations related to, among other things, hypothesis based solicitationand acquisition of at least a portion of objective occurrence data 70*including data indicating incidence of at least one objective occurrence71*. In some embodiments, the operational flow 300 may be executed by,for example, the computing device 10 of FIG. 1 b, which may be a serveror a standalone device. Alternatively, the operation flow 300 may beexecuted by, for example, the mobile device 30 of FIG. 1 a.

In FIG. 3 and in the following figures that include various examples ofoperational flows, discussions and explanations may be provided withrespect to the above-described exemplary environment of FIGS. 1 a and 1b, and/or with respect to other examples (e.g., as provided in FIGS. 2a-2 l) and contexts. However, it should be understood that theoperational flows may be executed in a number of other environments andcontexts, and/or in modified versions of FIGS. 1 a, 1 b, and 2 a-2 l.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders other than those which areillustrated, or may be performed concurrently.

Further, in FIG. 3 and in following figures, various operations may bedepicted in a box-within-a-box manner. Such depictions may indicate thatan operation in an internal box may comprise an optional exampleembodiment of the operational step illustrated in one or more externalboxes. However, it should be understood that internal box operations maybe viewed as independent operations separate from any associatedexternal boxes and may be performed in any sequence with respect to allother illustrated operations, or may be performed concurrently.

In any event, after a start operation, the operational flow 300 may moveto an objective occurrence data solicitation operation 302 forsoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence. For instance, the objective occurrence datasolicitation module 101 of the computing device 10 or the objectiveoccurrence data solicitation module 101′ of the mobile device 30soliciting, based at least in part on a hypothesis 77 (e.g., thecomputing device 10 referencing a hypothesis 77, or the mobile device 30receiving a request for soliciting at least a portion of objectiveoccurrence data from the computing device 10, the request being remotelygenerated by the computing device 10 and sent to the mobile device 30based at least in part on a hypothesis 77) that links one or moreobjective occurrences with one or more subjective user states (e.g., agroup of users 20* ingesting a particular type of medicine such asaspirin, and the subsequent subjective physical states, such as painrelief, associated with the group of users 20*) and in response at leastin part to an incidence of at least one subjective user state (e.g.,pain relief by a user 20*) associated with a user 20*, at least aportion of objective occurrence data 70* including data indicatingincidence of at least one objective occurrence 71* (e.g., ingestion ofaspirin by user 20*).

Note that the solicitation of at least a portion of the objectiveoccurrence data 70*, as described above, may or may not be in referenceto solicitation of particular data that indicates an incidence oroccurrence of a particular or particular type of objective occurrence.That is, in some embodiments, the solicitation of at least a portion ofthe objective occurrence data 70* may be in reference to solicitationfor objective occurrence data 70* including data indicating incidence ofany objective occurrence with respect to, for example, a particularpoint in time or time interval or with respect to a incidence of aparticular subjective user state associated with the user 20*. While inother embodiments, the solicitation of at least a portion of theobjective occurrence data 70* may involve soliciting for data indicatingoccurrence of a particular or particular type of objective occurrence.

The term “soliciting,” as will be used herein, may be in reference todirect or indirect solicitation of (e.g., requesting to be providedwith, requesting to access, gathering of, or other methods of beingprovided with or being allowed access to) at least a portion ofobjective occurrence data 70* from one or more sources. The sources forat least a portion of the objective occurrence data 70* may be a user20* (e.g., providing objective occurrence data 70 c via mobile device30), a mobile device 30 (e.g., mobile device 30 may have previouslyobtained the objective occurrence data 70 c from the user 20 a or fromother sources), one or more network servers (not depicted), one or morethird party sources 50 (e.g., providing objective occurrence data 70 a),or one or more sensors 35 (e.g., providing objective occurrence data 70b).

For example, if the computing device 10 is a server, then the computingdevice 10 may indirectly solicit at least a portion of objectiveoccurrence data 70 c from a user 20 a by transmitting, for example, arequest for at least the portion of the objective occurrence data 70 cto the mobile device 30, which in turn may solicit at least the portionof the objective occurrence data 70 c from the user 20 a. Alternatively,such data may have already been provided to the mobile device 30, inwhich case the mobile device 30 merely provides for or allows access tosuch data. Note that the objective occurrence data 70 c that may beprovided by the mobile device 30 may have originally been obtained fromthe user 20 a, from one or more third party sources 50, and/or from oneor more remote network devices (e.g., sensors 35 or network servers).

In some situations, at least a portion of objective occurrence data 70*may be stored in a network server (not depicted), and such a networkserver may be solicited for at least portion of the objective occurrencedata 70*. In other implementations, objective occurrence data 70 a or 70b may be solicited from one or more third party sources 50 (e.g., one ormore third parties or one or more network devices such as servers thatare associated with one or more third parties) or from one or moresensors 35. In yet other implementations in which the computing device10 is a standalone device, such as a handheld device to be used directlyby a user 20 b, the computing device 10 may directly solicit, forexample, the objective occurrence data 70 c from the user 20 b.

Operational flow 300 may further include an objective occurrence dataacquisition operation 304 for acquiring the objective occurrence dataincluding the data indicating incidence of at least one objectiveoccurrence. For instance, the objective occurrence data acquisitionmodule 104* of the computing device 10 or the mobile device 30 acquiring(e.g., receiving or accessing by the computing device 10 or by themobile device 30) the objective occurrence data 70* including the dataindicating incidence of at least one objective occurrence 71*.

In various implementations, the objective occurrence data solicitationoperation 302 of FIG. 3 may include one or more additional operations asillustrated in FIGS. 4 a, 4 b, 4 c, 4 d, 4 e, 4 f, 4 g, 4 h, 4 i, and 4j. For example, in some implementations the objective occurrence datasolicitation operation 302 may include a requesting operation 402 forrequesting for the data indicating incidence of at least one objectiveoccurrence from the user as depicted in FIG. 4 a. For instance, therequesting module 202* of the computing device 10 or the mobile device30 (e.g., the requesting module 202 of the computing device 10 or therequesting module 202′ of the mobile device 30) requesting (e.g.,transmitting or indicating a request by the computing device 10 or bythe mobile device 30) for the data indicating incidence of at least oneobjective occurrence 71* (e.g., 71 a, 71 b, or 71 c) from the user 20*(e.g., user 20 a or user 20 b).

In various implementations, the requesting operation 402 may furtherinclude one or more additional operations. For example, in someimplementations, the requesting operation 402 may include an operation403 for requesting for the data indicating incidence of at least oneobjective occurrence via a user interface as depicted in FIG. 4 a. Forexample, the user interface requesting module 204* of the computingdevice 10 (e.g., when the computing device 10 is a standalone device) orthe mobile device 30 requesting for the data indicating incidence of atleast one objective occurrence 71 c via a user interface 122* (e.g. anaudio device including one or more speakers or a display device such asa display monitor or a touchscreen).

Operation 403, in turn, may further include an operation 404 forindicating a request for the data indicating incidence of at least oneobjective occurrence through at least a display device as depicted inFIG. 4 a. For example, the request indication module 205* of thecomputing device 10 or the mobile device 30 indicating (e.g.,displaying) a request for the data indicating incidence of at least oneobjective occurrence 71 c (e.g., what was consumed for dinner today bythe user 20* or whether the user 20* exercised today?) through at leasta display device (e.g., a display monitor such as a liquid crystaldisplay or a touchscreen).

In the same or different implementations, operation 403 may include anoperation 405 for indicating a request for the data indicating incidenceof at least one objective occurrence through at least an audio device asdepicted in FIG. 4 a. For example, the request indication module 205* ofthe computing device 10 or the mobile device 30 indicating a request forthe data indicating incidence of at least one objective occurrence 70*(e.g., what was the humidity today or was a hot fudge sundae consumedtoday?) through at least an audio device (e.g., an audio systemincluding one or more speakers).

In some implementations, the requesting operation 402 may include anoperation 406 for requesting for the data indicating incidence of atleast one objective occurrence via at least one of a wireless network ora wired network as depicted in FIG. 4 a. For example, the networkinterface requesting module 206* of the computing device 10 or themobile device 30 requesting for the data indicating incidence of atleast one objective occurrence 71* (e.g., data indicating blood pressureof the user 20* or data indicating an exercise routine executed by theuser 20*) via at least one of a wireless network or a wired network 40.Note that in the case where the computing device 10 is executingoperation 406, the data indicating incidence of at least one objectiveoccurrence 71* may be requested from the user 20*, from one or morethird party sources 50, from one or more sensors 35, or from othernetwork devices (e.g., network servers). In the case where the mobiledevice 30 is executing operation 406, the data indicating incidence ofat least one objective occurrence 71* may be requested from a user 20 a,from one or more third party sources 50, from one or more sensors 35, orfrom other network devices (e.g., network servers).

In various implementations, the requesting operation 402 may include anoperation 407 for requesting the user to select an objective occurrencefrom a plurality of indicated alternative objective occurrences asdepicted in FIG. 4 a. For example, the selection request module 214* ofthe computing device 10 or the mobile device 30 requesting the user 20*to select an objective occurrence from a plurality of indicatingalternative objective occurrences (e.g., as indicated via a userinterface 122*). For example, requesting a user 20* to select oneobjective occurrence from a list that includes cloudy weather, sunnyweather, high humidity, low humidity, high or low blood pressure,ingestion of a medicine such as aspirin, ingestion of a particular typeof food item such as beer, an exercise routine such as jogging, and soforth.

In some implementations, operation 407 may further include an operation408 for requesting the user to select an objective occurrence from aplurality of indicated alternative contrasting objective occurrences asdepicted in FIG. 4 a. For example, the selection request module 214* ofthe computing device 10 or the mobile device 30 requesting the user 20*(e.g., either user 20 a or user 20 b) to select an objective occurrencefrom a plurality of indicated alternative contrasting objectiveoccurrences (e.g., as indicated via a user interface 122*). For example,requesting a user 20* to select one objective occurrence from a list ofindicated alternative contrasting objective occurrences such as runningfor 1 hour, running for 30 minutes, running for 15 minutes, walking for1 hour, walking for 30 minutes, sitting for 1 hour, sitting for 30minutes, and so forth.

In some implementations, the requesting operation 402 may include anoperation 409 for requesting the user to confirm incidence of the atleast one objective occurrence as depicted in FIG. 4 a. For example, theconfirmation request module 216* of the computing device 10 or themobile device 30 requesting the user 20* to confirm incidence of the atleast one objective occurrence (e.g., did user 20* have a salad forlunch today?).

In some implementations, the requesting operation 402 may include anoperation 410 for requesting the user to provide an indication of anincidence of at least one objective occurrence that occurred during aspecified point in time as depicted in FIG. 4 a. For example, therequesting module 202* of the computing device 10 or the mobile device30 requesting the user 20* (e.g., either user 20 a or user 20 b) toprovide an indication of an incidence of at least one objectiveoccurrence that occurred during a specified point in time (e.g., askingthe user 20* whether the user 20* ate dinner at a particular Mexicanrestaurant at 8 PM?).

In some implementations, the requesting operation 402 may include anoperation 411 for requesting the user to provide an indication of anincidence of at least one objective occurrence that occurred during aspecified time interval as depicted in FIG. 4 a. For example, therequesting module 202* of the computing device 10 or the mobile device30 requesting the user 20* to provide an indication of an incidence ofat least one objective occurrence that occurred during a specified timeinterval (e.g., asking the user 20* whether the user 20* slept between11 PM to 7 AM?).

In some implementations, the requesting operation 402 may include anoperation 412 for requesting the user to indicate an incidence of atleast one objective occurrence with respect to the incidence of the atleast one subjective user state associated with the user as depicted inFIG. 4 b. For instance, the requesting module 202* of the computingdevice 10 or the mobile device 30 requesting the user 20* (e.g., eitheruser 20 a or user 20 b) to indicate an incidence of at least oneobjective occurrence with respect to the incidence of the at least onesubjective user state associated with the user 20*. For example, askingthe user 20* to indicate what the weather was like when the user 20*felt depressed.

In various implementations, the requesting operation 402 may include anoperation 413 for providing a motivation for requesting for the dataindicating incidence of at least one objective occurrence as depicted inFIG. 4 b. For instance, the motivation provision module 212* of thecomputing device 10 or the mobile device 30 providing a motivation forrequesting for the data indicating incidence of at least one objectiveoccurrence 71 c (e.g., last time the user 20* was depressed, the weatherwas very bad).

In some implementations, operation 413 may include an operation 414 forproviding a motivation for requesting for the data indicating incidenceof at least one objective occurrence, the motivation relating to thelink between the one or more objective occurrences with the one or moresubjective user states as provided by the hypothesis as depicted in FIG.4 b. For instance, the motivation provision module 212* of the computingdevice 10 or the mobile device 30 providing a motivation for requestingfor the data indicating incidence of at least one objective occurrence71 c, the motivation relating to the link between the one or moreobjective occurrences with the one or more subjective user states asprovided by the hypothesis 77 (e.g., hypothesis linking depression withbad weather).

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude a requesting operation 415 for requesting for the dataindicating incidence of at least one objective occurrence from one ormore third party sources as depicted in FIG. 4 b. For instance, therequesting module 202* of the computing device 10 or the mobile device30 requesting (e.g., via at least one of a wireless network or a wirednetwork 40) for the data indicating incidence of at least one objectiveoccurrence 71 a from one or more third party sources 50.

In various implementations, the requesting operation 415 may include oneor more additional operations. For example, in some implementations, therequesting operation 415 may include an operation 416 for requesting forthe data indicating incidence of at least one objective occurrence fromone or more third party sources via at least one of a wireless networkor a wired network as depicted in FIG. 4 b. For instance, the networkinterface requesting module 206* of the computing device 10 or themobile device 30 requesting for the data indicating incidence of atleast one objective occurrence 71 a from one or more third party sources50 via at least one of a wireless network or a wired network 40.

In some implementations, the requesting operation 415 may include anoperation 417 for requesting the one or more third party sources toconfirm incidence of the at least one objective occurrence as depictedin FIG. 4 b. For instance, the confirmation request module 216* of thecomputing device 10 or the mobile device 30 requesting the one or morethird party sources 50* to confirm incidence of the at least oneobjective occurrence (e.g., asking a fitness center or a network deviceassociated with the fitness center whether the user 20* exercised on thetreadmill for 30 minutes on Tuesday).

In some implementations, the requesting operation 415 may include anoperation 418 for requesting the one or more third party sources toprovide an indication of an incidence of at least one objectiveoccurrence that occurred during a specified point in time as depicted inFIG. 4 b. For instance, the requesting module 202* of the computingdevice 10 or the mobile device 30 requesting the one or more third partysources 50 to provide an indication of an incidence of at least oneobjective occurrence that occurred during a specified point in time. Forexample, requesting from a content provider an indication of the localweather for 10 AM Tuesday).

In some implementations, the requesting operation 415 may include anoperation 419 for requesting the one or more third party sources toprovide an indication of an incidence of at least one objectiveoccurrence that occurred during a specified time interval as depicted inFIG. 4 b. For instance, the requesting module 202* of the computingdevice 10 or the mobile device 30 requesting the one or more third partysources 50 to provide an indication of an incidence of at least oneobjective occurrence that occurred during a specified time interval. Forexample, requesting from a content provider for an indication of theperformance of the stock market between 9 AM and 1 PM on Tuesday.

In some implementations, the requesting operation 415 may include anoperation 420 for requesting the one or more third party sources toprovide an indication of an incidence of at least one objectiveoccurrence that occurred with respect to the incidence of the at leastone subjective user state associated with the user as depicted in FIG. 4c. For instance, the requesting module 202* of the computing device 10or the mobile device 30 requesting the one or more third party sources50 (e.g., spouse of user 20*) to provide an indication of an incidenceof at least one objective occurrence (e.g., excessive snoring whilesleeping) that occurred with respect to the incidence of the at leastone subjective user state (e.g., sleepiness or fatigue) associated withthe user 20*.

In some implementations, the requesting operation 415 may include anoperation 421 for requesting for the data indicating incidence of atleast one objective occurrence from one or more content providers asdepicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more content providers (e.g., weather channel, internet news service,and so forth).

In some implementations, the requesting operation 415 may include anoperation 422 for requesting for the data indicating incidence of atleast one objective occurrence from one or more other users as depictedin FIG. 4 c. For instance, the requesting module 202* of the computingdevice 10 or the mobile device 30 requesting for the data indicatingincidence of at least one objective occurrence 71 a from one or moreother users (e.g., spouse, relatives, friends, or co-workers of user20*).

In some implementations, the requesting operation 415 may include anoperation 423 for requesting for the data indicating incidence of atleast one objective occurrence from one or more health care entities asdepicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more health care entities (e.g., medical doctors, dentists, healthcare facilities, clinics, hospitals, and so forth).

In some implementations, the requesting operation 415 may include anoperation 424 for requesting for the data indicating incidence of atleast one objective occurrence from one or more health fitness entitiesas depicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more health fitness entities (e.g., fitness gyms or fitnessinstructors).

In some implementations, the requesting operation 415 may include anoperation 425 for requesting for the data indicating incidence of atleast one objective occurrence from one or more business entities asdepicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more business entities (e.g., user 20* place of employment,merchandiser, airlines, and so forth).

In some implementations, the requesting operation 415 may include anoperation 426 for requesting for the data indicating incidence of atleast one objective occurrence from one or more social groups asdepicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more social groups (e.g., PTA, social networking groups, societies,clubs, and so forth).

In some implementations, the requesting operation 415 may include anoperation 427 for requesting for the data indicating incidence of atleast one objective occurrence from one or more third party sources viaa network interface as depicted in FIG. 4 c. For instance, therequesting module 202* of the computing device 10 or the mobile device30 requesting for the data indicating incidence of at least oneobjective occurrence 71 a from one or more third party sources 50 via anetwork interface 120*.

In some implementations, the requesting operation 415 may include anoperation 428 for requesting for the data indicating incidence of atleast one objective occurrence from one or more third party sourcesthrough at least one of a wireless network or a wired network asdepicted in FIG. 4 c. For instance, the requesting module 202* of thecomputing device 10 or the mobile device 30 requesting for the dataindicating incidence of at least one objective occurrence 71 a from oneor more third party sources 50 through at least one of a wirelessnetwork or a wired network 40.

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude an operation 429 for requesting for the data indicatingincidence of at least one objective occurrence from one or more remotedevices as depicted in FIG. 4 d. For instance, the network interfacerequesting module 206* of the computing device 10 or the mobile device30 requesting for the data indicating incidence of at least oneobjective occurrence 71 b from one or more remote devices (e.g., networkservers, sensors 35, mobile devices including mobile device 30, and/orother network devices).

Operation 429, in turn, may include one or more additional operations invarious alternative implementations. For example, in someimplementations, operation 429 may include an operation 430 fortransmitting a request to be provided with the data indicating incidenceof at least one objective occurrence to the one or more remote devicesas depicted in FIG. 4 d. For instance, the request transmission module207* of the computing device 10 or the mobile device 30 transmitting arequest to be provided with the data indicating incidence of at leastone objective occurrence 71 b to one or more remote devices (e.g.,network servers, sensors 35, mobile devices including mobile device 30,and/or other network devices).

In some implementations, operation 429 may include an operation 431 fortransmitting a request to have access to the data indicating incidenceof at least one objective occurrence to the one or more remote devicesas depicted in FIG. 4 d. For instance, the request access module 208* ofthe computing device 10 or the mobile device 30 transmitting a requestto have access to the data indicating incidence of at least oneobjective occurrence 71 b to the one or more remote devices (e.g.,network servers, sensors 35, mobile devices including mobile device 30in the case where operation 431 is performed by the computing device 10and the computing device 10 is a server, and/or other network devices).

In some implementations, operation 429 may include an operation 432 forconfiguring one or more remote devices to provide the data indicatingincidence of at least one objective occurrence as depicted in FIG. 4 d.For instance, the configuration module 209* of the computing device 10or the mobile device 30 configuring, via at least one of a wirelessnetwork or wired network 40, one or more remote devices (e.g., networkservers, mobile devices including mobile device 30, sensors 35, or othernetwork devices) to provide the data indicating incidence of at leastone objective occurrence 71 b.

In some implementations, operation 429 may include an operation 433 fordirecting or instructing the one or more remote devices to provide thedata indicating incidence of at least one objective occurrence asdepicted in FIG. 4 d. For instance, the directing/instructing module210* of the computing device 10 or the mobile device 30 directing orinstructing, via at least one of a wireless network or wired network 40,the one or more remote devices (e.g., network servers, mobile devicesincluding mobile device 35, sensors 35, or other network devices) toprovide the data indicating incidence of at least one objectiveoccurrence 71 b.

In some implementations, operation 429 may include an operation 434 forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more sensors as depicted in FIG. 4 d. Forinstance, the network interface requesting module 206* of the computingdevice 10 or the mobile device 30 requesting for the data indicatingincidence of at least one objective occurrence 71 b from one or moresensors 35 (e.g., GPS, physiological measuring device such as a bloodpressure device or glucometer).

In some implementations, operation 429 may include an operation 435 forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more network servers as depicted in FIG. 4 d. Forinstance, the network interface requesting module 206* of the computingdevice 10 or the mobile device 30 requesting for the data indicatingincidence of at least one objective occurrence 71 b from one or morenetwork servers, which may have previously obtained such data.

In some implementations, operation 429 may include an operation 436 forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more mobile devices as depicted in FIG. 4 d. Forinstance, the network interface requesting module 206* of the computingdevice 10 or the mobile device 30 requesting for the data indicatingincidence of at least one objective occurrence 71 b from one or moremobile devices (e.g., cellular telephone, PDA, laptop or notebook, andso forth) including, for example, mobile device 30.

In some implementations, operation 429 may include an operation 437 forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more remote devices through at least one of awireless network or a wired network as depicted in FIG. 4 d. Forinstance, the network interface requesting module 206* of the computingdevice 10 or the mobile device 30 requesting for the data indicatingincidence of at least one objective occurrence 71 b from one or moreremote network devices through at least one of a wireless network or awired network 40.

In some implementations, operation 429 may include an operation 438 forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more remote devices via a network interface asdepicted in FIG. 4 d. For instance, the network interface requestingmodule 206* of the computing device 10 or the mobile device 30requesting for the data indicating incidence of at least one objectiveoccurrence 71 b from one or more remote network devices via a networkinterface 120*.

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude an operation 439 for requesting to be provided with a time stampassociated with the incidence of at least one objective occurrence asdepicted in FIG. 4 e. For instance, the time/temporal element requestmodule 218* of the computing device 10 or the mobile device 30requesting to be provided with a time stamp associated with theincidence of at least one objective occurrence (e.g., requesting a timestamp associated with the user 20* consuming a particular medication).

In some implementations, the solicitation operation 302 may include anoperation 440 for requesting to be provided with an indication of a timeinterval associated with the incidence of at least one objectiveoccurrence as depicted in FIG. 4 e. For instance, the time/temporalelement request module 218* of the computing device 10 or the mobiledevice 30 requesting to be provided with an indication of a timeinterval associated with the incidence of at least one objectiveoccurrence (e.g., requesting to be provided with an indication thatindicates the time interval in which the user 20* exercised on thetreadmill).

In some implementations, the solicitation operation 302 may include anoperation 441 for requesting to be provided with an indication of atemporal relationship between the incidence of the at least onesubjective user state associated with the user and the incidence of theat least one objective occurrence as depicted in FIG. 4 e. For instance,the time/temporal element request module 218* of the computing device 10or the mobile device 30 requesting to be provided with an indication ofa temporal relationship between the incidence of the at least onesubjective user state associated with the user 20* and the incidence ofthe at least one objective occurrence (e.g., did user 20* eat at theMexican restaurant before, after, or as the user 20* was having theupset stomach?).

In some implementations, the solicitation operation 302 may include anoperation 442 for soliciting data indicating an ingestion by the user ofa medicine as depicted in FIG. 4 e. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting (e.g., via a network interface 120* orvia a user interface 122*) data indicating an ingestion by the user 20*of a medicine (e.g., what type of medicine was ingested on Wednesdaymorning?).

In some implementations, the solicitation operation 302 may include anoperation 443 for soliciting data indicating an ingestion by the user ofa food item as depicted in FIG. 4 e. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting (e.g., via a network interface 120* orvia a user interface 122*) data indicating an ingestion by the user 20*of a food item (e.g., what did the user 20* eat for lunch?).

In some implementations, the solicitation operation 302 may include anoperation 444 for soliciting data indicating an ingestion by the user ofa nutraceutical as depicted in FIG. 4 e. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting (e.g., via a network interface 120* orvia a user interface 122*) data indicating an ingestion by the user 20*of a nutraceutical (e.g., what type of nutraceutical did the user 20*eat on Tuesday?).

In some implementations, the solicitation operation 302 may include anoperation 445 for soliciting data indicating an exercise routineexecuted by the user as depicted in FIG. 4 e. For instance, theobjective occurrence data solicitation module 101* of the computingdevice 10 or the mobile device 30 soliciting (e.g., via a networkinterface 120* or via a user interface 122*) data indicating an exerciseroutine executed by the user 20* (e.g., what type of exercise did theuser 20* do today?).

In some implementations, the solicitation operation 302 may include anoperation 446 for soliciting data indicating a social activity executedby the user as depicted in FIG. 4f. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting (e.g., via a network interface 120* orvia a user interface 122*) data indicating a social activity executed bythe user 20*. For example, asking the user 20* or a third party (e.g.,another user) whether the user 20* went with friends to a nightclub.

In some implementations, the solicitation operation 302 may include anoperation 447 for soliciting data indicating an activity performed byone or more third parties as depicted in FIG. 4 f. For instance, theobjective occurrence data solicitation module 101* of the computingdevice 10 or the mobile device 30 soliciting (e.g., via a networkinterface 120* or via a user interface 122*) data indicating an activityperformed by one or more third parties (e.g., boss going on vacation).For example, asking the user 20* or a third party (e.g., another user)whether the user 20* went on a vacation.

In some implementations, the solicitation operation 302 may include anoperation 448 for soliciting data indicating one or more physicalcharacteristics of the user as depicted in FIG. 4 f. For instance, theobjective occurrence data solicitation module 101* of the computingdevice 10 or the mobile device 30 soliciting (e.g., via a networkinterface 120* or via a user interface 122*) data indicating one or morephysical characteristics (e.g., blood pressure) of the user 20*. Forexample, requesting the user 20*, a third party source 50 (e.g., aphysician), or a sensor 35 to provide data indicating blood pressure ofthe user 20*.

In some implementations, the solicitation operation 302 may include anoperation 449 for soliciting data indicating a resting, a learning, or arecreational activity by the user as depicted in FIG. 4 f. For instance,the objective occurrence data solicitation module 101* of the computingdevice 10 or the mobile device 30 soliciting (e.g., via a networkinterface 120* or via a user interface 122*) data indicating a resting(e.g., sleeping), a learning (e.g., attending a class or reading abook), or a recreational activity (e.g., playing golf or fishing) by theuser 20*.

In some implementations, the solicitation operation 302 may include anoperation 450 for soliciting data indicating occurrence of one or moreexternal events as depicted in FIG. 4 f. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting (e.g., via a network interface 120* orvia a user interface 122*) data indicating occurrence of one or moreexternal events (e.g., poor weather or poor stock market performance).For example requesting the user 20* or one or more third party sources50 such as content providers to provide indications of the local weatheror performance of the stock market.

In some implementations, the solicitation operation 302 may include anoperation 451 for soliciting data indicating one or more locations ofthe user as depicted in FIG. 4 f. For instance, the objective occurrencedata solicitation module 101* of the computing device 10 or the mobiledevice 30 soliciting (e.g., via a network interface 120* or via a userinterface 122*) data indicating one or more locations of the user 20*.For example requesting the user 20* or a sensor 35 such as a GPS toprovide one or more locations of the user 20*.

In some implementations, the solicitation operation 302 may include anoperation 452 for soliciting data indicating incidence of at least oneobjective occurrence that occurred during a specified point in time asdepicted in FIG. 4 f. For instance, the objective occurrence datasolicitation module 101* of the computing device 10 or the mobile device30 soliciting (e.g., via a network interface 120* or via a userinterface 122*) data indicating incidence of at least one objectiveoccurrence that occurred during a specified point in time (e.g., askingwhat the user 20* ate at noon).

In some implementations, the solicitation operation 302 may include anoperation 453 for soliciting data indicating incidence of at least oneobjective occurrence that occurred during a specified time interval asdepicted in FIG. 4 f. For instance, the objective occurrence datasolicitation module 101* of the computing device 10 or the mobile device30 soliciting (e.g., via a network interface 120* or via a userinterface 122*) data indicating incidence of at least one objectiveoccurrence 71* that occurred during a specified time interval (e.g.,asking whether the user 20* consumed any medication between 8 PM andmidnight).

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude operations that may be particularly performed by the computingdevice 10. For example, in some implementations, the solicitationoperation 302 may include an operation 454 for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing the hypothesis as depicted in FIG. 4 g.For instance, the objective occurrence data solicitation module 101 ofthe computing device 10 soliciting the data indicating incidence of atleast one objective occurrence 71* based, at least in part, on thehypothesis referencing module 220 referencing the hypothesis 77.

Operation 454, in various implementations, may further include one ormore additional operations. For example, in some implementations,operation 454 may include an operation 455 for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing a hypothesis that identifies one or moretemporal relationships between the one or more objective occurrences andthe one or more subjective user states as depicted in FIG. 4 g. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies oneor more temporal relationships between the one or more objectiveoccurrences and the one or more subjective user states. For example, thehypothesis 77 may indicate that a person may feel more alert afterexercising vigorously for one hour.

In some cases, operation 455 may further include an operation 456 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies one or more time sequential relationships between the atleast one subjective user state and the one or more objectiveoccurrences as depicted in FIG. 4 g. For instance, the objectiveoccurrence data solicitation module 101 of the computing device 10soliciting the data indicating incidence of at least one objectiveoccurrence 71* based, at least in part, on the hypothesis referencingmodule 220 referencing a hypothesis 77 that identifies one or more timesequential relationships between the at least one subjective user stateand the one or more objective occurrences. For example, the hypothesis77 may indicate that a person may develop a stomach ache two hours aftereating a hot fudge sundae.

In some implementations, operation 454 may include an operation 457 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between at least an ingestion of a medicineand the one or more subjective user states as depicted in FIG. 4 g. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies arelationship between at least an ingestion of a medicine (e.g., aspirin)and the one or more subjective user states (e.g., pain relief).

In some implementations, operation 454 may include an operation 458 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between at least an ingestion of a food itemand the one or more subjective user states as depicted in FIG. 4 g. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies arelationship between at least an ingestion of a food item (e.g., papaya)and the one or more subjective user states (e.g., bowel movement).

In some implementations, operation 454 may include an operation 459 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between at least an ingestion of anutraceutical and the one or more subjective user states as depicted inFIG. 4 g. For instance, the objective occurrence data solicitationmodule 101 of the computing device 10 soliciting the data indicatingincidence of at least one objective occurrence 71* based, at least inpart, on the hypothesis referencing module 220 referencing a hypothesis77 that identifies a relationship between at least an ingestion of anutraceutical and the one or more subjective user states.

In some implementations, operation 454 may include an operation 460 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between execution of one or more exerciseroutines and the one or more subjective user states as depicted in FIG.4 h. For instance, the objective occurrence data solicitation module 101of the computing device 10 soliciting the data indicating incidence ofat least one objective occurrence 71* based, at least in part, on thehypothesis referencing module 220 referencing a hypothesis 77 thatidentifies a relationship between execution of one or more exerciseroutines (e.g., playing basketball) and the one or more subjective userstates (e.g., painful ankles).

In some implementations, operation 454 may include an operation 461 forsoliciting the data indicating incidence of at least one subjective userstate associated with the user based, at least in part, on referencing ahypothesis that identifies a relationship between execution of one ormore social activities and the one or more subjective user states asdepicted in FIG. 4 h. For instance, the objective occurrence datasolicitation module 101 of the computing device 10 soliciting the dataindicating incidence of at least one objective occurrence 71* based, atleast in part, on the hypothesis referencing module 220 referencing ahypothesis 77 that identifies a relationship between execution of one ormore social activities (e.g., playing with offspring) and the one ormore subjective user states (e.g., happiness).

In some implementations, operation 454 may include an operation 462 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between one or more activities executed by athird party and the one or more subjective user states as depicted inFIG. 4 h. For instance, the objective occurrence data solicitationmodule 101 of the computing device 10 soliciting the data indicatingincidence of at least one objective occurrence 71* based, at least inpart, on the hypothesis referencing module 220 referencing a hypothesis77 that identifies a relationship between one or more activitiesexecuted by a third party (in-laws visiting) and the one or moresubjective user states (e.g., tension).

In some implementations, operation 454 may include an operation 463 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between one or more physical characteristicsof the user and the one or more subjective user states as depicted inFIG. 4 h. For instance, the objective occurrence data solicitationmodule 101 of the computing device 10 soliciting the data indicatingincidence of at least one objective occurrence 71* based, at least inpart, on the hypothesis referencing module 220 referencing a hypothesis77 that identifies a relationship between one or more physicalcharacteristics (e.g., low blood sugar level) of the user 20* and theone or more subjective user states (e.g., lack of alertness).

In some implementations, operation 454 may include an operation 464 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between a resting, a learning, or arecreational activity performed by the user and the one or moresubjective user states as depicted in FIG. 4 h. For instance, theobjective occurrence data solicitation module 101 of the computingdevice 10 soliciting the data indicating incidence of at least oneobjective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies arelationship between a resting, a learning, or a recreational activityperformed by the user 20* and the one or more subjective user states.

In some implementations, operation 454 may include an operation 465 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between one or more external activities andthe one or more subjective user states as depicted in FIG. 4 h. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies arelationship between one or more external activities (e.g., poorperformance of a sports team) and the one or more subjective user states(e.g., depression).

In some implementations, operation 454 may include an operation 466 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between one or more locations of the user andthe one or more subjective user states as depicted in FIG. 4 i. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that identifies arelationship between one or more locations (e.g., Hawaii) of the user20* and the one or more subjective user states (e.g., relaxation).

In some implementations, operation 454 may include an operation 467 forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatlinks the at least one subjective user state with one or more historicalobjective occurrences as depicted in FIG. 4 i. For instance, theobjective occurrence data solicitation module 101 of the computingdevice 10 soliciting the data indicating incidence of at least oneobjective occurrence 71* based, at least in part, on the hypothesisreferencing module 220 referencing a hypothesis 77 that links the atleast one subjective user state (e.g., hangover) with one or morehistorical objective occurrences (e.g., alcohol consumption).

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude operations that may be particularly suited to be executed by themobile device 30 of FIG. 1 a rather than by, for example, the computingdevice 10 of FIG. 1 b. For instance, in some implementations thesolicitation operation 302 of FIG. 3 may include an operation 468 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response to a reception of a request to solicit the dataindicating incidence of at least one objective occurrence, the requestto solicit being remotely generated based, at least in part, on thehypothesis as depicted in FIG. 4 i. For instance, the objectiveoccurrence data solicitation module 101′ of the mobile device 30soliciting the data indicating incidence of at least one objectiveoccurrence 71* in response to the request to solicit reception module270 receiving a request to solicit the data indicating incidence of atleast one objective occurrence 71*, the request to solicit beingremotely generated (e.g., remotely generated by the computing device 10)based, at least in part, on the hypothesis 77. In various alternativeimplementations, the objective occurrence data solicitation module 101′of the mobile device 30 may solicit the data indicating incidence of atleast one objective occurrence 71* from a user 20 a, from one or moresensors 35, or from one or more third party sources 50.

Operation 468, in turn, may further include one or more additionaloperations. For example, in some implementations, operation 468 mayinclude an operation 469 for soliciting the data indicating incidence ofat least one objective occurrence in response to a reception of arequest to solicit the data indicating incidence of at least oneobjective occurrence, the request to solicit being remotely generatedbased, at least in part, on the hypothesis and in response to theincidence of the at least one subjective user state associated with theuser as depicted in FIG. 4 i. For instance, the objective occurrencedata solicitation module 101′ of the mobile device 30 soliciting thedata indicating incidence of at least one objective occurrence 71* inresponse to the request to solicit reception module 270 receiving arequest to solicit the data indicating incidence of at least oneobjective occurrence 71*, the request to solicit being remotelygenerated based, at least in part, on the hypothesis 77 (e.g., ahypothesis linking upset stomach to ingestion of Mexican cuisine) and inresponse to the incidence of the at least one subjective user state(upset stomach) associated with the user 20 a. In some implementations,such an incidence may have been initially reported by the user 20 a via,for example, user interface 122′.

In some implementations, operation 468 may include an operation 470 forreceiving the request to solicit the data indicating incidence of atleast one objective occurrence via at least one of a wireless network ora wired network as depicted by FIG. 4 i. For instance, the request tosolicit reception module 270 of the mobile device 30 receiving therequest to solicit the data indicating incidence of at least oneobjective occurrence 71* via at least one of a wireless network or awired network 40.

Operation 470, in turn, may include an operation 471 for receiving therequest to solicit the data indicating incidence of at least oneobjective occurrence from a network server as depicted by FIG. 4 i. Forinstance, the request to solicit reception module 270 of the mobiledevice 30 receiving the request to solicit the data indicating incidenceof at least one objective occurrence 71* from a network server (e.g.,computing device 10).

In various implementations, the solicitation operation 302 of FIG. 3 mayinclude an operation 472 for soliciting the data indicating incidence ofat least one objective occurrence in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate associated with the user as depicted in FIG. 4 j. For instance,the objective occurrence data solicitation module 101* of the computingdevice 10 or the mobile device 30 soliciting the data indicatingincidence of at least one objective occurrence 71* in response, at leastin part, to the subjective user state data reception module 224*receiving (e.g., via the network interface 120* or via the userinterface 122*) data indicating incidence of the at least one subjectiveuser state 61* associated with the user 20*.

In some implementations, operation 472 may further include an operation473 for soliciting the data indicating incidence of at least oneobjective occurrence in response, at least in part, to receiving dataindicating incidence of the at least one subjective user stateassociated with the user via a user interface as depicted in FIG. 4 j.For instance, the objective occurrence data solicitation module 101* ofthe computing device 10 or the mobile device 30 soliciting the dataindicating incidence of at least one objective occurrence 71* inresponse, at least in part, to the subjective user state data receptionmodule 224* receiving data indicating incidence of the at least onesubjective user state 61* associated with the user 20* via a userinterface 122*.

In some implementations, operation 472 may include an operation 474 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser via a network interface as depicted in FIG. 4 j. For instance, theobjective occurrence data solicitation module 101 of the computingdevice 10 soliciting the data indicating incidence of at least oneobjective occurrence 71* in response, at least in part, to thesubjective user state data reception module 224 receiving dataindicating incidence of the at least one subjective user state 61 aassociated with the user 20 a via a network interface 120.

In some implementations, operation 472 may include an operation 475 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser via one or more blog entries as depicted in FIG. 4 j. For instance,the objective occurrence data solicitation module 101 of the computingdevice 10 soliciting the data indicating incidence of at least oneobjective occurrence 71* in response, at least in part, to receivingdata indicating incidence of the at least one subjective user state 61 aassociated with the user 20 a via one or more blog entries.

In some implementations, operation 472 may include an operation 476 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser via one or more status reports as depicted in FIG. 4 j. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* in response, at least in part, to receivingdata indicating incidence of the at least one subjective user state 61 aassociated with the user 20 a via one or more status reports.

In some implementations, operation 472 may include an operation 477 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser via one or more electronic messages as depicted in FIG. 4 j. Forinstance, the objective occurrence data solicitation module 101 of thecomputing device 10 soliciting the data indicating incidence of at leastone objective occurrence 71* in response, at least in part, to receivingdata indicating incidence of the at least one subjective user state 61 aassociated with the user 20 a via one or more electronic messages.

In some implementations, operation 472 may include an operation 478 forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser from the user as depicted in FIG. 4 j. For instance, the objectiveoccurrence data solicitation module 101* of the computing device 10 orthe mobile device 30 soliciting the data indicating incidence of atleast one objective occurrence 71* in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate 61* associated with the user 20* from the user 20*.

Referring back to FIG. 3, the objective occurrence data acquisitionoperation 304 may include one or more additional operations in variousalternative implementations. For example, in various implementations,the objective occurrence data acquisition operation 304 may include areception operation 502 for receiving the objective occurrence dataincluding the data indicating incidence of at least one objectiveoccurrence as depicted in FIG. 5 a. For instance, the objectiveoccurrence data reception module 234* of the computing device 10 or themobile device 30 receiving (e.g., via the user interface 122* or via atleast one of a wireless network or wired network 40) the objectiveoccurrence data 70* including the data indicating incidence of at leastone objective occurrence 71*.

In various alternative implementations, the reception module 502 mayinclude one or more additional operations. For example, in someimplementations, the reception operation 502 may include an operation504 for receiving the objective occurrence data including the dataindicating incidence of at least one objective occurrence via a userinterface as depicted in FIG. 5 a. For instance, the user interface datareception module 235* of the computing device 10 or the mobile device 30receiving the objective occurrence data 70* including the dataindicating incidence of at least one objective occurrence 71* via a userinterface 122* (e.g., a microphone, a keypad, a touchscreen, and soforth).

In some implementations, the reception operation 502 may include anoperation 506 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence from atleast one of a wireless network or a wired network as depicted in FIG. 5a. For instance, the network interface data reception module 236* of thecomputing device 10 or the mobile device 30 receiving the objectiveoccurrence data 70* including the data indicating incidence of at leastone objective occurrence 71* from at least one of a wireless network ora wired network 40.

In some implementations, the reception operation 502 may include anoperation 510 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence via oneor more blog entries as depicted in FIG. 5 a. For instance, the networkinterface data reception module 236* of the computing device 10 or themobile device 30 receiving the objective occurrence data 70* includingthe data indicating incidence of at least one objective occurrence 71*via one or more blog entries (e.g., microblog entries).

In some implementations, the reception operation 502 may include anoperation 512 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence via oneor more status reports as depicted in FIG. 5 a. For instance, thenetwork interface data reception module 236* of the computing device 10or the mobile device 30 receiving the objective occurrence data 70*including the data indicating incidence of at least one objectiveoccurrence 71* via one or more status reports (e.g., social networkingstatus reports).

In some implementations, the reception operation 502 may include anoperation 514 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence via oneor more electronic messages as depicted in FIG. 5 a. For instance, thenetwork interface data reception module 236* of the computing device 10or the mobile device 30 receiving the objective occurrence data 70*including the data indicating incidence of at least one objectiveoccurrence 71* via one or more electronic messages (e.g., text messages,email messages, IM messages, or other types of messages).

In some implementations, the reception operation 502 may include anoperation 516 for receiving a selection made by the user, the selectionbeing a selection of an objective occurrence from a plurality ofindicated alternative objective occurrences as depicted in FIG. 5 b. Forinstance, the objective occurrence data reception module 234* of thecomputing device 10 or the mobile device 30 receiving a selection madeby the user 20*, the selection being a selection of an objectiveoccurrence from a plurality of indicated alternative objectiveoccurrences (e.g., as indicated via a user interface 122*).

In some implementations, the reception operation 502 may include anoperation 518 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence from theuser as depicted in FIG. 5 b. For instance, the objective occurrencedata reception module 234* of the computing device 10 or the mobiledevice 30 receiving the objective occurrence data 70 c including thedata indicating incidence of at least one objective occurrence 71 c fromthe user 20*.

In some implementations, the reception operation 502 may include anoperation 520 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence from oneor more third party sources as depicted in FIG. 5 b. For instance, theobjective occurrence data reception module 234* of the computing device10 or the mobile device 30 receiving the objective occurrence data 70 aincluding the data indicating incidence of at least one objectiveoccurrence 71 a from one or more third party sources 50 (e.g., otherusers, content providers, health care providers, health fitnessproviders, social organizations, business, and so forth).

In some implementations, the reception operation 502 may include anoperation 522 for receiving the objective occurrence data including thedata indicating incidence of at least one objective occurrence from oneor more remote devices as depicted in FIG. 5 b. For instance, theobjective occurrence data reception module 234* of the computing device10 or the mobile device 30 receiving the objective occurrence data 70 bincluding the data indicating incidence of at least one objectiveoccurrence 71 b from one or more remote devices (e.g., sensors 35 orremote network servers).

In some implementations, the objective occurrence data acquisitionoperation 304 of FIG. 3 may include an operation 524 for acquiring dataindicating an ingestion by the user of a medicine as depicted in FIG. 5c. For instance, the objective occurrence data acquisition module 104*of the computing device 10 or the mobile device 30 acquiring (e.g.,receiving, retrieving, or accessing) data indicating an ingestion by theuser 20* of a medicine (e.g., a dosage of a beta blocker).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 526 for acquiring data indicatingan ingestion by the user of a food item as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104* of thecomputing device 10 or the mobile device 30 acquiring data indicating aningestion by the user 20* of a food item (e.g., a fruit).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 528 for acquiring data indicatingan ingestion by the user of a nutraceutical as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104* of thecomputing device 10 or the mobile device 30 acquiring data indicating aningestion by the user 20* of a nutraceutical (e.g. broccoli).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 530 for acquiring data indicatingan exercise routine executed by the user as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104* of thecomputing device 10 or the mobile device 30 acquiring data indicating anexercise routine (e.g., exercising on an exercise machine such as atreadmill) executed by the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 532 for acquiring data indicatinga social activity executed by the user as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104* of thecomputing device 10 or the mobile device 30 acquiring data indicating asocial activity (e.g., hiking or skiing with friends, dates, dinners,and so forth) executed by the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 534 for acquiring data indicatingan activity performed by one or more third parties as depicted in FIG. 5c. For instance, the objective occurrence data acquisition module 104*of the computing device 10 or the mobile device 30 acquiring dataindicating an activity performed by one or more third parties (e.g.,spouse leaving home to visit relatives).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 536 for acquiring data indicatingone or more physical characteristics of the user as depicted in FIG. 5c. For instance, the objective occurrence data acquisition module 104*of the computing device 10 or the mobile device 30 acquiring dataindicating one or more physical characteristics (e.g., blood sugar orblood pressure level) of the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 538 for acquiring data indicatinga resting, a learning, or a recreational activity by the user asdepicted in FIG. 5 c. For instance, the objective occurrence dataacquisition module 104* of the computing device 10 or the mobile device30 acquiring data indicating a resting (e.g., napping), a learning(e.g., attending a lecture), or a recreational activity (e.g., boating)by the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 540 for acquiring data indicatingoccurrence of one or more external events as depicted in FIG. 5 c. Forinstance, the objective occurrence data acquisition module 104* of thecomputing device 10 or the mobile device 30 acquiring data indicatingoccurrence of one or more external events (e.g., sub-freezing weather).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 542 for acquiring data indicatingone or more locations of the user as depicted in FIG. 5 d. For instance,the objective occurrence data acquisition module 104* of the computingdevice 10 or the mobile device 30 acquiring data indicating one or morelocations of the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 544 for acquiring data indicatingincidence of at least one objective occurrence that occurred during aspecified point in time as depicted in FIG. 5 d. For instance, theobjective occurrence data acquisition module 104* of the computingdevice 10 or the mobile device 30 acquiring data indicating incidence ofat least one objective occurrence 71* that occurred during a specifiedpoint in time (e.g., as specified through a user interface 122*).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 546 for acquiring data indicatingincidence of at least one objective occurrence that occurred during aspecified time interval as depicted in FIG. 5 d. For instance, theobjective occurrence data acquisition module 104* of the computingdevice 10 or the mobile device 30 acquiring data indicating incidence ofat least one objective occurrence that occurred during a specified timeinterval (e.g., as specified through a user interface 122*).

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 548 for acquiring data indicatingincidence of at least one objective occurrence at a server as depictedin FIG. 5 d. For instance, when the computing device 10 is a server andacquires the data indicating incidence of at least one objectiveoccurrence 71*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 550 for acquiring data indicatingincidence of at least one objective occurrence at a handheld device asdepicted in FIG. 5 d. For instance, when the computing device 10 is astandalone device and is a handheld device or when the mobile device 30is a handheld device and acquires the data indicating incidence of atleast one objective occurrence 71*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 552 for acquiring data indicatingincidence of at least one objective occurrence at a peer-to-peer networkcomponent device as depicted in FIG. 5 d. For instance, when thecomputing device 10 is a standalone device and is a peer-to-peer networkcomponent device or the mobile device 30 is a peer-to-peer networkcomponent device and acquires the data indicating incidence of at leastone objective occurrence 71*.

In some implementations, the objective occurrence data acquisitionoperation 304 may include an operation 554 for acquiring data indicatingincidence of at least one objective occurrence via a Web 2.0 constructas depicted in FIG. 5 d. For instance, when the computing device 10 orthe mobile device 30 is running a web 2.0 application 268 and acquiresthe data indicating incidence of at least one objective occurrence 71*.

Referring to FIG. 6 illustrating another operational flow 600 inaccordance with various embodiments. Operational flow 600 includescertain operations that mirror the operations included in operationalflow 300 of FIG. 3. For example, operational flow 600 includes anobjective occurrence data solicitation operation 602 and an objectiveoccurrence data acquisition operation 604 that correspond to and mirrorthe objective occurrence data solicitation operation 302 and theobjective occurrence data acquisition operation 304, respectively, ofFIG. 3.

In addition, and unlike operation 300 of FIG. 3, operational flow 600may additionally include a subjective user state data acquisitionoperation 606 for acquiring subjective user state data including dataindicating incidence of the at least one subjective user stateassociated with the user as depicted in FIG. 6. For instance, thesubjective user state data acquisition module 102* of the computingdevice 10 or the mobile device 30 acquiring (e.g., receiving, gathering,or retrieving via the network interface 120* or via the user interface122*) subjective user state data 60* including data indicating incidenceof the at least one subjective user state 61* associated with the user20*.

In various alternative implementations, the subjective user state dataacquisition operation 606 may include one or more additional operations.For example, in some implementations, the subjective user state dataacquisition operation 606 may include a reception operation 702 forreceiving the subjective user state data as depicted in FIG. 7 a. Forinstance, the subjective user state data reception module 224* of thecomputing device 10 or the mobile device 30 receiving the subjectiveuser state data 60*.

The reception operation 702, in turn, may include one or more additionaloperations in various alternative implementations. For example, in someimplementations, the reception operation 702 may include an operation704 for receiving the subjective user state data via a user interface asdepicted in FIG. 7 a. For instance, the user interface reception module226* of the computing device 10 (e.g., when the computing device 10 is astandalone device) or the mobile device 30 receiving the subjective userstate data 60* via a user interface 122* (e.g., a keyboard, a mouse, atouchscreen, a microphone, an image capturing device such as a digitalcamera, and/or other interface devices).

In some implementations, the reception operation 702 may include anoperation 706 for receiving the subjective user state data from at leastone of a wireless network or a wired network as depicted in FIG. 7 a.For instance, the network interface reception module 227 of thecomputing device 10 (e.g., when the computing device 10 is a server)receiving the subjective user state data 60* from at least one of awireless network or a wired network 40.

In some implementations, the reception operation 702 may include anoperation 708 for receiving the subjective user state data via one ormore blog entries as depicted in FIG. 7 a. For instance, the networkinterface reception module 227 of the computing device 10 (e.g., whenthe computing device 10 is a server) receiving the subjective user statedata 60* via one or more blog entries (e.g., microblog entries).

In some implementations, the reception operation 702 may include anoperation 710 for receiving the subjective user state data via one ormore status reports as depicted in FIG. 7 a. For instance, the networkinterface reception module 227 of the computing device 10 (e.g., whenthe computing device 10 is a server) receiving the subjective user statedata 60* via one or more status reports (e.g., social networking statusreports).

In some implementations, the reception operation 702 may include anoperation 712 for receiving the subjective user state data via one ormore electronic messages as depicted in FIG. 7 a. For instance, thenetwork interface reception module 227 of the computing device 10 (e.g.,when the computing device 10 is a server) receiving the subjective userstate data 60* via one or more electronic messages (e.g., text message,email message, audio or text message, IM message, or other types ofelectronic messages).

In some implementations, the reception operation 702 may include anoperation 714 for receiving the subjective user state data from the useras depicted in FIG. 7 a. For instance, the subjective user state datareception module 224* of the computing device 10 or the mobile device 30receiving the subjective user state data 60* from the user 20*.

Operation 714, in turn, may further include an operation 716 forreceiving the subjective user state data from the user via one or moreremote network devices as depicted in FIG. 7 a. For instance, thenetwork interface reception module 227 of the computing device 10 (e.g.,when the computing device 10 is a server) receiving the subjective userstate data 60 a from the user 20 a via one or more remote networkdevices (e.g., mobile device 30 or other devices such as other networkservers).

In some implementations, the reception operation 702 may include anoperation 718 for receiving a selection made by the user, the selectionbeing a selection of a subjective user state from a plurality ofindicated alternative subjective user states as depicted in FIG. 7 a.For instance, the subjective user state data reception module 224* ofthe computing device 10 or the mobile device 30 receiving (e.g.,receiving from at least one of a wireless network or a wired network 40or via a user interface 122*) a selection made by the user 20*, theselection being a selection of a subjective user state from a pluralityof indicated alternative subjective user states (e.g., as indicatedthrough a user interface 122*).

In various implementations, the subjective user state data acquisitionoperation 606 of FIG. 6 may include an operation 720 for acquiring dataindicating at least one subjective mental state associated with the useras depicted in FIG. 7 a. For instance, the subjective user state dataacquisition module 102* of the computing device 10 or the mobile device30 acquiring (e.g., receiving, retrieving, or accessing) data indicatingat least one subjective mental state (e.g., happiness, sadness,depression, anger, frustration, elation, fear, alertness, sleepiness,envy, and so forth) associated with the user 20*.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 722 for acquiring data indicatingat least one subjective physical state associated with the user asdepicted in FIG. 7 a. For instance, the subjective user state dataacquisition module 102* of the computing device 10 or the mobile device30 acquiring (e.g., receiving, retrieving, or accessing) data indicatingat least one subjective physical state (e.g., pain, blurring vision,hearing loss, upset stomach, physical exhaustion, and so forth)associated with the user 20*.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 724 for acquiring data indicatingat least one subjective overall state associated with the user asdepicted in FIG. 7 b. For instance, the subjective user state dataacquisition module 102* of the computing device 10 or the mobile device30 acquiring (e.g., receiving, retrieving, or accessing) data indicatingat least one subjective overall state (e.g., good, bad, well, lousy, andso forth) associated with the user 20*.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 726 for acquiring a time stampassociated with the incidence of the at least one subjective user stateas depicted in FIG. 7 b. For instance, the time stamp acquisition module230* of the computing device 10 or the mobile device 30 acquiring (e.g.,receiving or generating) a time stamp associated with the incidence ofthe at least one subjective user state.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 728 for acquiring an indicationof a time interval associated with the incidence of the at least onesubjective user state as depicted in FIG. 7 b. For instance, the timeinterval acquisition module 231* of the computing device 10 or themobile device 30 acquiring (e.g., receiving or generating) an indicationof a time interval associated with the incidence of the at least onesubjective user state.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 730 for acquiring the subjectiveuser state data at a server as depicted in FIG. 7 b. For instance, whenthe computing device 10 is a network server and is acquiring thesubjective user state data 60 a.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 732 for acquiring the subjectiveuser state data at a handheld device as depicted in FIG. 7 b. Forinstance, when the computing device 10 is a standalone device and is ahandheld device (e.g., a cellular telephone, a smartphone, an MID, anUMPC, or a convergent device such as a PDA) or the mobile device 30 is ahandheld device, and the computing device 10 or the mobile device 30 isacquiring the subjective user state data 60*.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 734 for acquiring the subjectiveuser state data at a peer-to-peer network component device as depictedin FIG. 7 b. For instance, when the computing device 10 or the mobiledevice 30 is a peer-to-peer network component device and the computingdevice 10 or the mobile device 30 is acquiring the subjective user statedata 60*.

In some implementations, the subjective user state data acquisitionoperation 606 may include an operation 736 for acquiring the subjectiveuser state data via a Web 2.0 construct as depicted in FIG. 7 b. Forinstance, when the computing device 10 or the mobile device 30 acquiresthe subjective user state data 60* via a Web 2.0 construct (e.g., Web2.0 application 268).

Referring now to FIG. 8 illustrating still another operational flow 800in accordance with various embodiments. In some embodiments, operationalflow 800 may be particularly suited to be performed by the computingdevice 10, which may be a network server or a standalone device.Operational flow 800 includes operations that mirror the operationsincluded in the operational flow 600 of FIG. 6. For example, operationalflow 800 may include an objective occurrence data solicitation operation802, an objective occurrence data acquisition operation 804, and asubjective user state data acquisition operation 806 that corresponds toand mirror the objective occurrence data solicitation operation 602, theobjective occurrence data acquisition operation 604, and the subjectiveuser state data acquisition operation 606, respectively, of FIG. 6.

In addition, and unlike operational flow 600, operational flow 800 mayfurther include a correlation operation 808 for correlating thesubjective user state data with the objective occurrence data and apresentation operation 810 for presenting one or more results of thecorrelating of the subjective user state data with the objectiveoccurrence data as depicted in FIG. 8. For instance, the correlationmodule 106 of the computing device 10 correlating (e.g., linking ordetermining a relationship between) the subjective user state data 60*with the objective occurrence data 70*. The presentation module 108 ofthe computing device 10 may then present (e.g., transmit via a networkinterface 120 or indicate via a user interface 122) one or more resultsof the correlation operation 808 performed by the correlation module106.

In various alternative implementations, the correlation operation 808may include one or more additional operations. For example, in someimplementations, the correlation operation 808 may include an operation902 for correlating the subjective user state data with the objectiveoccurrence data based, at least in part, on a determination of at leastone sequential pattern associated with the at least one subjective userstate and the at least one objective occurrence as depicted in FIG. 9.For instance, the correlation module 106 of the computing device 10correlating the subjective user state data 60* with the objectiveoccurrence data 70* based, at least in part, on the sequential patterndetermination module 242 determining at least one sequential patternassociated with the at least one subjective user state indicated by thesubjective user state data 60* and the at least one objective occurrenceindicated by the objective occurrence data 70*.

Operation 902, in turn, may further include one or more additionaloperations. For example, in some implementations, operation 902 mayinclude an operation 904 for correlating the subjective user state datawith the objective occurrence data based, at least in part, onreferencing historical data as depicted in FIG. 9. For instance, thecorrelation module 106 of the computing device 10 correlating thesubjective user state data 60* with the objective occurrence data 70*based, at least in part, on the historical data referencing module 243referencing historical data 78. Examples of historical data 78 includes,for example, previously reported incidences of subjective user statesassociated with the user 20* and/or with other users as they relate toobjective occurrences, historical sequential patterns associated withthe user 20* or with other users, historical medical data relating tothe user 20 and/or other users, and/or other types of historical data78.

In some implementations, operation 904 may include an operation 906 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on a historical sequential pattern asfurther depicted in FIG. 9. For instance, the correlation module 106 ofthe computing device 10 correlating the subjective user state data 60*with the objective occurrence data 70* based, at least in part, on thehistorical data referencing module 243 referencing a historicalsequential pattern associated with the user 20*, with other users,and/or with a subset of the general population.

In some implementations, operation 904 may include an operation 908 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on referencing historical medical data asdepicted in FIG. 9. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60* withthe objective occurrence data 70* based, at least in part, on thehistorical data referencing module 243 referencing historical medicaldata (e.g., genetic, metabolome, or proteome information or medicalrecords of the user 20* or of others related to, for example, diabetesor heart disease).

In various implementations, operation 902 may include an operation 910for comparing the at least one sequential pattern to a second sequentialpattern to determine whether the at least one sequential pattern atleast substantially matches with the second sequential pattern asdepicted in FIG. 9. For instance, the sequential pattern comparisonmodule 248 of the computing device 10 comparing the at least onesequential pattern to a second sequential pattern to determine whetherthe at least one sequential pattern at least substantially matches withthe second sequential pattern.

Operation 910, in some implementations, may further include an operation912 for comparing the at least one sequential pattern to a secondsequential pattern related to at least a second subjective user stateassociated with the user and a second objective occurrence to determinewhether the at least one sequential pattern at least substantiallymatches with the second sequential pattern as depicted in FIG. 9. Forinstance, the sequential pattern comparison module 248 of the computingdevice 10 comparing the at least one sequential pattern to a secondsequential pattern related to at least a previously reported secondsubjective user state associated with the user 20* and a secondpreviously reported objective occurrence to determine whether the atleast one sequential pattern at least substantially matches with thesecond sequential pattern.

For these implementations, the comparison of the first sequentialpattern to the second sequential pattern may involve making certaincomparisons, For example, comparing the first subjective user state tothe second subjective user state to determine at least whether they arethe same or different types of subjective user states. Similarly, thefirst objective occurrence may be compared to the second objectiveoccurrence to determine at least whether they are the same or differenttypes of objective occurrences. The temporal relationship or thespecific time sequencing between the incidence of the first subjectiveuser state and the incidence of the first objective occurrence (e.g., asrepresented by the first sequential pattern) may then be compared to thetemporal relationship or the specific time sequencing between theincidence of the second subjective user state and the incidence of thesecond objective occurrence (e.g., as represented by the secondsequential pattern).

In some implementations, the correlation operation 808 of FIG. 8 mayinclude an operation 914 for correlating the subjective user state datawith the objective occurrence data at a server as depicted in FIG. 9.For instance, when the computing device 10 is a server (e.g., networkserver) and the correlation module 106 correlates the subjective userstate data 60* with the objective occurrence data 70*.

In alternative implementations, the correlation operation 808 mayinclude an operation 916 for correlating the subjective user state datawith the objective occurrence data at a handheld device as depicted inFIG. 9. For instance, when the computing device 10 is a standalonedevice, such as a handheld device, and the correlation module 106correlates the subjective user state data 60* with the objectiveoccurrence data 70*.

In some implementations, the correlation operation 808 may include anoperation 918 for correlating the subjective user state data with theobjective occurrence data at a peer-to-peer network component device asdepicted in FIG. 9. For instance, when the computing device 10 is astandalone device and is a peer-to-peer network component device, andthe correlation module 106 correlates the subjective user state data 60*with the objective occurrence data 70*.

Referring back to FIG. 8, the presentation operation 810 may include oneor more additional operations in various alternative implementations.For example, in some implementations, the presentation operation 810 mayinclude an operation 1002 for indicating the one or more results of thecorrelating via a user interface as depicted in FIG. 10. For instance,when the computing device 10 is a standalone device such as a handhelddevice (e.g., cellular telephone, a smartphone, an MID, an UMPC, aconvergent device, and so forth) or other mobile devices, and the userinterface indication module 259 of the computing device 10 indicates theone or more results of the correlation operation performed by thecorrelation module 106 via a user interface 122 (e.g., display monitoror audio system including a speaker).

In some implementations, the presentation operation 810 may include anoperation 1004 for transmitting the one or more results of thecorrelating via a network interface as depicted in FIG. 10. Forinstance, when the computing device 10 is a server and the networkinterface transmission module 258 of the computing device 10 transmitsthe one or more results of the correlation operation performed by thecorrelation module 106 via a network interface 120 (e.g., NIC).

In some implementations, the presentation operation 810 may include anoperation 1006 for presenting an indication of a sequential relationshipbetween the at least one subjective user state and the at least oneobjective occurrence as depicted in FIG. 10. For instance, thesequential relationship presentation module 260 of the computing device10 presenting (e.g., either by transmitting via the network interface120 or by indicating via the user interface 122) an indication of asequential relationship between the at least one subjective user state(e.g., happy) and the at least one objective occurrence (e.g., playingwith children).

In some implementations, the presentation operation 810 may include anoperation 1008 for presenting a prediction of a future subjective userstate resulting from a future objective occurrence associated with theuser as depicted in FIG. 10. For instance, the prediction presentationmodule 261 of the computing device 10 presenting (e.g., either bytransmitting via the network interface 120 or by indicating via the userinterface 122) a prediction of a future subjective user state associatedwith the user 20* resulting from a future objective occurrence (e.g.,“if you drink the 24 ounces of beer you ordered, you will have ahangover tomorrow”).

In some implementations, the presentation operation 810 may include anoperation 1010 for presenting a prediction of a future subjective userstate resulting from a past objective occurrence associated with theuser as depicted in FIG. 10. For instance, the prediction presentationmodule 261 of the computing device 10 presenting (e.g., either bytransmitting via the network interface 120 or by indicating via the userinterface 122) a prediction of a future subjective user state associatedwith the user 20* resulting from a past objective occurrence (e.g., “youwill have a stomach ache shortly because of the hot fudge sundae thatyou just ate”).

In some implementations, the presentation operation 810 may include anoperation 1012 for presenting a past subjective user state in connectionwith a past objective occurrence associated with the user as depicted inFIG. 10. For instance, the past presentation module 262 of the computingdevice 10 presenting (e.g., either by transmitting via the networkinterface 120 or by indicating via the user interface 122) a pastsubjective user state associated with the user 20* in connection with apast objective occurrence (e.g., “reason why you had a headache thismorning may be because you drank that 24 ounces of beer last night”).

In some implementations, the presentation operation 810 may include anoperation 1014 for presenting a recommendation for a future action asdepicted in FIG. 10. For instance, the recommendation module 263 of thecomputing device 10 presenting (e.g., either by transmitting via thenetwork interface 120 or by indicating via the user interface 122) arecommendation for a future action (e.g., “you should buy something tocalm your stomach tonight after you leave the bar tonight”).

In some implementations, operation 1014 may further include an operation1016 for presenting a justification for the recommendation as depictedin FIG. 10. For instance, the justification module 264 of the computingdevice 10 presenting (e.g., either by transmitting via the networkinterface 120 or by indicating via the user interface 122) ajustification for the recommendation (e.g., “you should buy something tocalm your stomach tonight since you are drinking beer tonight, and thelast time you drank beer, you had an upset stomach the next morning”).

In some implementations, the presentation operation 810 may include anoperation 1018 for presenting the hypothesis as depicted in FIG. 10. Forinstance, the hypothesis presentation module 267 of the computing device10 presenting (e.g., via the user interface 122 or via the networkinterface 120) the hypothesis 77 to, for example, the user 20* or to oneor more third parties. Such an operation may be performed in some caseswhen the data indicating incidence of at least one objective occurrence71* that was solicited is acquired and confirms or provides support forthe hypothesis 77.

FIG. 11 illustrates another operational flow 1100 in accordance withvarious embodiments. In contrast to the previous operational flow 800,operational flow 1100 may be particularly suited to be performed by amobile device 30 rather than by the computing device 10. Operationalflow 1100 includes certain operations that may completely orsubstantially mirror certain operations included in the operational flow800 of FIG. 8. For example, operational flow 1100 may include anobjective occurrence data solicitation operation 1102, an objectiveoccurrence data acquisition operation 1104, and a presentation operation1110 that corresponds to and completely or substantially mirror theobjective occurrence data solicitation operation 802, the objectiveoccurrence data acquisition operation 804, and the presentationoperation 810, respectively, of FIG. 8.

In addition, and unlike operational flow 800, operational flow 1100 mayfurther include an objective occurrence data transmission operation 1106for transmitting the acquired objective occurrence data including thedata indicating incidence of at least one objective occurrence and areception operation 1108 for receiving one or more results ofcorrelation of the objective occurrence data with subjective user statedata including data indicating the incidence of the at least onesubjective user state associated with the user as depicted in FIG. 11.For example, the objective occurrence data transmission module 160 ofthe mobile device 30 transmitting (e.g., transmitting via at least oneof the wireless network or wired network 40 to, for example, a networkserver such as computing device 10) the acquired objective occurrencedata 70* including the data indicating incidence of at least oneobjective occurrence 71*. Note that the mobile device 30 may, itself,have originally acquired the data indicating incidence of at least oneobjective occurrence 71* from the user 20 a, from one or more sensors35, or from one or more third party sources 50.

The correlation results reception module 162 of the mobile device 30 maythen receive (e.g., receive from the computing device 10) one or moreresults of correlation of the subjective user state data 60 a withobjective occurrence data 70* including data indicating the incidence ofthe at least one objective occurrence 71*.

In various alternative implementations, the objective occurrence datatransmission operation 1106 may include one or more additionaloperations. For example, in some implementations, the objectiveoccurrence data transmission operation 1106 may include an operation1202 for transmitting the acquired objective occurrence data via atleast a wireless network or a wired network as depicted in FIG. 12. Forinstance, the objective occurrence data transmission module 160 of themobile device 30 transmitting the acquired objective occurrence data 70*via at least one of a wireless network or a wired network 40.

In some implementations, operation 1202 may further include an operation1204 for transmitting the acquired objective occurrence data via one ormore blog entries as depicted in FIG. 12. For instance, the objectiveoccurrence data transmission module 160 of the mobile device 30transmitting the acquired objective occurrence data 70* via one or moreblog entries (e.g., microblog entries).

In some implementations, operation 1202 may include an operation 1206for transmitting the acquired objective occurrence data via one or morestatus reports as depicted in FIG. 12. For instance, the objectiveoccurrence data transmission module 160 of the mobile device 30transmitting the acquired objective occurrence data 70* via one or morestatus reports (e.g., social networking status reports).

In some implementations, operation 1202 may include an operation 1208for transmitting the acquired objective occurrence data via one or moreelectronic messages as depicted in FIG. 12. For instance, the objectiveoccurrence data transmission module 160 of the mobile device 30transmitting the acquired objective occurrence data 70* via one or moreelectronic messages (e.g., email message, IM messages, text messages,and so forth).

In some implementations, operation 1202 may include an operation 1210for transmitting the acquired objective occurrence data to a networkserver as depicted in FIG. 12. For instance, the objective occurrencedata transmission module 160 of the mobile device 30 transmitting theacquired objective occurrence data 70* to a network server (e.g.,computing device 10).

Referring back to FIG. 11, the reception operation 1108 may include oneor more additional operations in various alternative implementations.For example, in some implementations, the reception operation 1108 mayinclude an operation 1302 for receiving an indication of a sequentialrelationship between the at least one subjective user state and the atleast one objective occurrence as depicted in FIG. 13. For instance, thecorrelation results reception module 162 of the mobile device 30receiving (e.g., via wireless network and/or wired network 40) at leastan indication of a sequential relationship between the at least onesubjective user state (e.g., as indicated by the data indicatingincidence of at least one subjective user state 61 a) and the at leastone objective occurrence (e.g., as indicated by the data indicatingincidence of at least one objective occurrence 71*). For example,receiving an indication that the user 20 a felt energized after joggingfor thirty minutes.

In some implementations, the reception operation 1108 may include anoperation 1304 for receiving a prediction of a future subjective userstate resulting from a future objective occurrence associated with theuser as depicted in FIG. 13. For instance, the correlation resultsreception module 162 of the mobile device 30 receiving (e.g., viawireless network and/or wired network 40) at least a prediction of afuture subjective user state (e.g., feeling energized) associated withthe user 20 a resulting from a future objective occurrence (e.g.,jogging for 30 minutes).

In some implementations, the reception operation 1108 may include anoperation 1306 for receiving a prediction of a future subjective userstate resulting from a past objective occurrence associated with theuser as depicted in FIG. 13. For instance, the correlation resultsreception module 162 of the mobile device 30 receiving (e.g., viawireless network and/or wired network 40) at least a prediction of afuture subjective user state (e.g., easing of pain) associated with theuser 20 a resulting from a past objective occurrence (e.g., previousingestion of aspirin).

In some implementations, the reception operation 1108 may include anoperation 1308 for receiving a past subjective user state in connectionwith a past objective occurrence as depicted in FIG. 13. For instance,the correlation results reception module 162 of the mobile device 30receiving (e.g., via wireless network and/or wired network 40) at leastan indication of a past subjective user state (e.g., depression)associated with the user 20 a in connection with a past objectiveoccurrence (e.g., overcast weather).

In some implementations, the reception operation 1108 may include anoperation 1310 for receiving a recommendation for a future action asdepicted in FIG. 13. For instance, the correlation results receptionmodule 162 of the mobile device 30 receiving (e.g., via wireless networkand/or wired network 40) at least a recommendation for a future action(e.g., “you should go to sleep early”).

In certain implementations, operation 1310 may further include anoperation 1312 for receiving a justification for the recommendation asdepicted in FIG. 13. For instance, the correlation results receptionmodule 162 of the mobile device 30 receiving (e.g., via wireless networkand/or wired network 40) at least a justification for the recommendation(e.g., “last time you stayed up late, you were very tired the nextmorning”).

In some implementations, the reception operation 1108 may include anoperation 1314 for receiving an indication of the hypothesis as depictedin FIG. 13. For instance, the correlation results reception module 162of the mobile device 30 receiving (e.g., via wireless network and/orwired network 40) an indication of the hypothesis 77. Such an operationmay be performed when, for example, the objective occurrence data 70*and the subjective user state data 60 a supports the hypothesis 77.

Referring back to FIG. 11, the process 1100 in various implementationsmay include a presentation operation 1110 to be performed by the mobiledevice 30 for presenting the one or more results of the correlation. Forexample, the presentation module 108′ of the mobile device 30 presentingthe one or more results of the correlation received by the correlationresults reception module 162. As described earlier, the presentationoperation 1110 of FIG. 11 in some implementations may completely orsubstantially mirror the presentation operation 810 of FIG. 8. Forinstance, in some implementations, the presentation operation 1110 mayinclude, similar to the presentation operation 810 of FIG. 8, anoperation 1402 for presenting the one or more results of the correlationvia a user interface as depicted in FIG. 14. For instance, the userinterface indication module 259′ of the mobile device 30 indicating theone or more results of the correlation via a user interface 122′ (e.g.,an audio device including one or more speakers and/or a display devicesuch as a LCD or a touchscreen).

In some implementations, operation 1402 may further include an operation1404 for indicating the one or more results of the correlation via atleast a display device as depicted in FIG. 14. For instance, the userinterface indication module 259′ of the mobile device 30 indicating theone or more results of the correlation via a display device (e.g., adisplay monitor such as a LCD or a touchscreen).

In some implementations, operation 1402 may include an operation 1406for indicating the one or more results of the correlation via at leastan audio device as depicted in FIG. 14. For instance, the user interfaceindication module 259′ of the mobile device 30 indicating the one ormore results of the correlation via an audio device (e.g., a speaker).

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware an d software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

1.-158. (canceled)
 159. A computationally-implemented system,comprising: means for soliciting, based at least in part on a hypothesisthat links one or more objective occurrences with one or more subjectiveuser states and in response at least in part to an incidence of at leastone subjective user state associated with a user, at least a portion ofobjective occurrence data including data indicating incidence of atleast one objective occurrence; and means for acquiring the objectiveoccurrence data including the data indicating incidence of at least oneobjective occurrence.
 160. The computationally-implemented system ofclaim 159, wherein said means for soliciting, based at least in part ona hypothesis that links one or more objective occurrences with one ormore subjective user states and in response at least in part to anincidence of at least one subjective user state associated with a user,at least a portion of objective occurrence data including dataindicating incidence of at least one objective occurrence comprises:means for requesting for the data indicating incidence of at least oneobjective occurrence from the user. 161.-164. (canceled)
 165. Thecomputationally-implemented system of claim 160, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from the user comprises: means for requesting the user toselect an objective occurrence from a plurality of indicated alternativeobjective occurrences.
 166. (canceled)
 167. Thecomputationally-implemented system of claim 160, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from the user comprises: means for requesting the user toconfirm incidence of the at least one objective occurrence. 168.-170.(canceled)
 171. The computationally-implemented system of claim 160,wherein said means for requesting for the data indicating incidence ofat least one objective occurrence from the user comprises: means forproviding a motivation for requesting for the data indicating incidenceof at least one objective occurrence.
 172. Thecomputationally-implemented system of claim 171, wherein said means forproviding a motivation for requesting for the data indicating incidenceof at least one objective occurrence comprises: means for providing amotivation for requesting for the data indicating incidence of at leastone objective occurrence, the motivation relating to the link betweenthe one or more objective occurrences with the one or more subjectiveuser states as provided by the hypothesis.
 173. Thecomputationally-implemented system of claim 159, wherein said means forsoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence comprises: means for requesting for the dataindicating incidence of at least one objective occurrence from one ormore third party sources.
 174. (canceled)
 175. Thecomputationally-implemented system of claim 173, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more third party sources comprises: means forrequesting the one or more third party sources to confirm incidence ofthe at least one objective occurrence. 176.-186. (canceled)
 187. Thecomputationally-implemented system of claim 159, wherein said means forsoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence comprises: means for requesting for the dataindicating incidence of at least one objective occurrence from one ormore remote devices.
 188. The computationally-implemented system ofclaim 187, wherein said means for requesting for the data indicatingincidence of at least one objective occurrence from one or more remotedevices comprises: means for transmitting a request to be provided withthe data indicating incidence of at least one objective occurrence tothe one or more remote devices.
 189. The computationally-implementedsystem of claim 187, wherein said means for requesting for the dataindicating incidence of at least one objective occurrence from one ormore remote devices comprises: means for transmitting a request to haveaccess to the data indicating incidence of at least one objectiveoccurrence to the one or more remote devices.
 190. Thecomputationally-implemented system of claim 187, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more remote devices comprises: means forconfiguring one or more remote devices to provide the data indicatingincidence of at least one objective occurrence.
 191. Thecomputationally-implemented system of claim 187, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more remote devices comprises: means fordirecting or instructing the one or more remote devices to provide thedata indicating incidence of at least one objective occurrence.
 192. Thecomputationally-implemented system of claim 187, wherein said means forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more remote devices comprises: means forrequesting for the data indicating incidence of at least one objectiveoccurrence from one or more sensors. 193.-209. (canceled)
 210. Thecomputationally-implemented system of claim 159, wherein said means forsoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence comprises: means for soliciting data indicatingincidence of at least one objective occurrence that occurred during aspecified point in time.
 211. The computationally-implemented system ofclaim 159, wherein said means for soliciting, based at least in part ona hypothesis that links one or more objective occurrences with one ormore subjective user states and in response at least in part to anincidence of at least one subjective user state associated with a user,at least a portion of objective occurrence data including dataindicating incidence of at least one objective occurrence comprises:means for soliciting data indicating incidence of at least one objectiveoccurrence that occurred during a specified time interval.
 212. Thecomputationally-implemented system of claim 159, wherein said means forsoliciting, based at least in part on a hypothesis that links one ormore objective occurrences with one or more subjective user states andin response at least in part to an incidence of at least one subjectiveuser state associated with a user, at least a portion of objectiveoccurrence data including data indicating incidence of at least oneobjective occurrence comprises: means for soliciting the data indicatingincidence of at least one objective occurrence based, at least in part,on referencing the hypothesis.
 213. The computationally-implementedsystem of claim 212, wherein said means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing the hypothesis comprises: means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies one or more temporal relationships between the one or moreobjective occurrences and the one or more subjective user states. 214.The computationally-implemented system of claim 213, wherein said meansfor soliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies one or more temporal relationships between the one or moreobjective occurrences and the one or more subjective user statescomprises: means for soliciting the data indicating incidence of atleast one objective occurrence based, at least in part, on referencing ahypothesis that identifies one or more time sequential relationshipsbetween the at least one subjective user state and the one or moreobjective occurrences.
 215. The computationally-implemented system ofclaim 212, wherein said means for soliciting the data indicatingincidence of at least one objective occurrence based, at least in part,on referencing the hypothesis comprises: means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing a hypothesis that identifies arelationship between at least an ingestion of a medicine and the one ormore subjective user states.
 216. The computationally-implemented systemof claim 212, wherein said means for soliciting the data indicatingincidence of at least one objective occurrence based, at least in part,on referencing the hypothesis comprises: means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing a hypothesis that identifies arelationship between at least an ingestion of a food item and the one ormore subjective user states.
 217. The computationally-implemented systemof claim 212, wherein said means for soliciting the data indicatingincidence of at least one objective occurrence based, at least in part,on referencing the hypothesis comprises: means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing a hypothesis that identifies arelationship between at least an ingestion of a nutraceutical and theone or more subjective user states.
 218. The computationally-implementedsystem of claim 212, wherein said means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing the hypothesis comprises: means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing a hypothesis thatidentifies a relationship between execution of one or more exerciseroutines and the one or more subjective user states.
 219. Thecomputationally-implemented system of claim 212, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing the hypothesiscomprises: means for soliciting the data indicating incidence of atleast one subjective user state associated with the user based, at leastin part, on referencing a hypothesis that identifies a relationshipbetween execution of one or more social activities and the one or moresubjective user states.
 220. The computationally-implemented system ofclaim 212, wherein said means for soliciting the data indicatingincidence of at least one objective occurrence based, at least in part,on referencing the hypothesis comprises: means for soliciting the dataindicating incidence of at least one objective occurrence based, atleast in part, on referencing a hypothesis that identifies arelationship between one or more activities executed by a third partyand the one or more subjective user states.
 221. Thecomputationally-implemented system of claim 212, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing the hypothesiscomprises: means for soliciting the data indicating incidence of atleast one objective occurrence based, at least in part, on referencing ahypothesis that identifies a relationship between one or more physicalcharacteristics of the user and the one or more subjective user states.222. The computationally-implemented system of claim 212, wherein saidmeans for soliciting the data indicating incidence of at least oneobjective occurrence based, at least in part, on referencing thehypothesis comprises: means for soliciting the data indicating incidenceof at least one objective occurrence based, at least in part, onreferencing a hypothesis that identifies a relationship between aresting, a learning, or a recreational activity performed by the userand the one or more subjective user states.
 223. Thecomputationally-implemented system of claim 212, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing the hypothesiscomprises: means for soliciting the data indicating incidence of atleast one objective occurrence based, at least in part, on referencing ahypothesis that identifies a relationship between one or more externalactivities and the one or more subjective user states.
 224. Thecomputationally-implemented system of claim 212, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence based, at least in part, on referencing the hypothesiscomprises: means for soliciting the data indicating incidence of atleast one objective occurrence based, at least in part, on referencing ahypothesis that identifies a relationship between one or more locationsof the user and the one or more subjective user states.
 225. (canceled)226. The computationally-implemented system of claim 159, wherein saidmeans for soliciting, based at least in part on a hypothesis that linksone or more objective occurrences with one or more subjective userstates and in response at least in part to an incidence of at least onesubjective user state associated with a user, at least a portion ofobjective occurrence data including data indicating incidence of atleast one objective occurrence comprises: means for soliciting the dataindicating incidence of at least one objective occurrence in response toa reception of a request to solicit the data indicating incidence of atleast one objective occurrence, the request to solicit being remotelygenerated based, at least in part, on the hypothesis.
 227. Thecomputationally-implemented system of claim 226, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence in response to a reception of a request to solicit the dataindicating incidence of at least one objective occurrence, the requestto solicit being remotely generated based, at least in part, on thehypothesis comprises: means for soliciting the data indicating incidenceof at least one objective occurrence in response to a reception of arequest to solicit the data indicating incidence of at least oneobjective occurrence, the request to solicit being remotely generatedbased, at least in part, on the hypothesis and in response to theincidence of the at least one subjective user state associated with theuser. 228.-229. (canceled)
 230. The computationally-implemented systemof claim 159, wherein said means for soliciting, based at least in parton a hypothesis that links one or more objective occurrences with one ormore subjective user states and in response at least in part to anincidence of at least one subjective user state associated with a user,at least a portion of objective occurrence data including dataindicating incidence of at least one objective occurrence comprises:means for soliciting the data indicating incidence of at least oneobjective occurrence in response, at least in part, to receiving dataindicating incidence of the at least one subjective user stateassociated with the user. 231.-232. (canceled)
 233. Thecomputationally-implemented system of claim 230, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser comprises: means for soliciting the data indicating incidence of atleast one objective occurrence in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate associated with the user via one or more blog entries.
 234. Thecomputationally-implemented system of claim 230, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser comprises: means for soliciting the data indicating incidence of atleast one objective occurrence in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate associated with the user via one or more status reports.
 235. Thecomputationally-implemented system of claim 230, wherein said means forsoliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser comprises: means for soliciting the data indicating incidence of atleast one objective occurrence in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate associated with the user via one or more electronic messages. 236.The computationally-implemented system of claim 230, wherein said meansfor soliciting the data indicating incidence of at least one objectiveoccurrence in response, at least in part, to receiving data indicatingincidence of the at least one subjective user state associated with theuser comprises: means for soliciting the data indicating incidence of atleast one objective occurrence in response, at least in part, toreceiving data indicating incidence of the at least one subjective userstate associated with the user from the user.
 237. Thecomputationally-implemented system of claim 159, wherein said means foracquiring the objective occurrence data including the data indicatingincidence of at least one objective occurrence comprises: means forreceiving the objective occurrence data including the data indicatingincidence of at least one objective occurrence. 238.-239. (canceled)240. The computationally-implemented system of claim 237, wherein saidmeans for receiving the objective occurrence data including the dataindicating incidence of at least one objective occurrence comprises:means for receiving the objective occurrence data including the dataindicating incidence of at least one objective occurrence via one ormore blog entries.
 241. The computationally-implemented system of claim237, wherein said means for receiving the objective occurrence dataincluding the data indicating incidence of at least one objectiveoccurrence comprises: means for receiving the objective occurrence dataincluding the data indicating incidence of at least one objectiveoccurrence via one or more status reports.
 242. Thecomputationally-implemented system of claim 237, wherein said means forreceiving the objective occurrence data including the data indicatingincidence of at least one objective occurrence comprises: means forreceiving the objective occurrence data including the data indicatingincidence of at least one objective occurrence via one or moreelectronic messages. 243.-262. (canceled)
 263. Thecomputationally-implemented system of claim 159, further comprising:means for acquiring subjective user state data including data indicatingincidence of the at least one subjective user state associated with theuser. 264.-281. (canceled)
 282. The computationally-implemented systemof claim 263, further comprising: means for correlating the subjectiveuser state data with the objective occurrence data; and means forpresenting one or more results of the correlating of the subjective userstate data with the objective occurrence data. 283.-300. (canceled) 301.The computationally-implemented system of claim 159, further comprising:means for transmitting the acquired objective occurrence data includingthe data indicating incidence of at least one objective occurrence;means for receiving one or more results of correlation of the objectiveoccurrence data with subjective user state data including dataindicating the incidence of the at least one subjective user stateassociated with the user; and means for presenting the one or moreresults of the correlation. 302.-318. (canceled)